From e8c036b55f575c422b3e33754b6a234b102e749c Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Wed, 23 Oct 2024 05:00:31 +0000
Subject: [PATCH] Updated datasets 2024-10-23 UTC

---
 datasets/ABI_G16-STAR-L2P-v2.70_2.70.json     |     4 +-
 datasets/ABI_G16-STAR-L3C-v2.70_2.70.json     |     4 +-
 datasets/ABI_G17-STAR-L2P-v2.71_2.71.json     |     4 +-
 datasets/ABI_G17-STAR-L3C-v2.71_2.71.json     |     4 +-
 datasets/AHI_H08-STAR-L2P-v2.70_2.70.json     |     4 +-
 datasets/AHI_H08-STAR-L3C-v2.70_2.70.json     |     4 +-
 datasets/ALTIKA_SARAL_L2_OST_XOGDR_f.json     |     4 +-
 ...TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json |     4 +-
 ...AUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json |     4 +-
 datasets/AMSR2-REMSS-L2P-v8.2_8.2.json        |     4 +-
 datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json     |     4 +-
 datasets/AMSR2-REMSS-L3U-v8.2_8.2.json        |     4 +-
 datasets/AMSR2-REMSS-L3U-v8a_8a.json          |     4 +-
 datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json     |     4 +-
 datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json       |     4 +-
 datasets/AMSRE-REMSS-L2P-v7a_7a.json          |     4 +-
 datasets/AMSRE-REMSS-L3U-v7a_7a.json          |     4 +-
 ..._CRI_TIME_SERIES_RL06.1_V3_RL06.1Mv03.json |     4 +-
 .../AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0.json   |     4 +-
 ...TA-L2-25km_Operational_Near-Real-Time.json |     4 +-
 ...L2-Coastal_Operational_Near-Real-Time.json |     4 +-
 .../ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json    |     4 +-
 .../ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json  |     4 +-
 datasets/ASCATA_L2_25KM_CDR_1.0.json          |     4 +-
 datasets/ASCATA_L2_COASTAL_CDR_1.0.json       |     4 +-
 ...TB-L2-25km_Operational_Near-Real-Time.json |     4 +-
 ...L2-Coastal_Operational_Near-Real-Time.json |     4 +-
 .../ASCATB_ESDR_ANCILLARY_L2_V1.1_1.1.json    |     4 +-
 .../ASCATB_ESDR_L2_WIND_STRESS_V1.1_1.1.json  |     4 +-
 ...TC-L2-25km_Operational_Near-Real-Time.json |     4 +-
 ...L2-Coastal_Operational_Near-Real-Time.json |     4 +-
 datasets/ATLAS_DEALIASED_SASS_L2_1.json       |     4 +-
 datasets/AVHRR18_G-NAVO-L2P-v1.0_1.0.json     |     4 +-
 datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json       |     4 +-
 datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json       |     4 +-
 datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json   |     4 +-
 datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json   |     4 +-
 datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json   |     4 +-
 datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json   |     4 +-
 datasets/AVHRRF_MC-STAR-L2P-v2.80_2.80.json   |     4 +-
 datasets/AVHRRF_MC-STAR-L3U-v2.80_2.80.json   |     4 +-
 datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json    |     4 +-
 datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json    |     4 +-
 datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json    |     4 +-
 datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json    |     4 +-
 datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json    |     4 +-
 datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json  |     4 +-
 datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json  |     4 +-
 .../AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json  |     4 +-
 ...HRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json |     4 +-
 ...HRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json |     4 +-
 .../AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json  |     4 +-
 ...HRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json |     4 +-
 ...HRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json |     4 +-
 ...VHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json |     4 +-
 datasets/BAROCLINIC_HRET14_14.json            |     4 +-
 datasets/CAM5K30CF_003.json                   |     4 +-
 datasets/CAM5K30EM_001.json                   |     4 +-
 datasets/CAM5K30EM_003.json                   |     4 +-
 datasets/CAM5K30UC_001.json                   |     4 +-
 datasets/CAM5K30UC_003.json                   |     4 +-
 datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json   |     4 +-
 .../CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json    |     4 +-
 ...DDIS_GLONASS_daily_data_combinednav_1.json |     4 +-
 ...CDDIS_GLONASS_daily_data_compactobs_1.json |     4 +-
 datasets/CDDIS_GLONASS_daily_data_met_1.json  |     4 +-
 datasets/CDDIS_GLONASS_daily_data_obs_1.json  |     4 +-
 datasets/CDDIS_GLONASS_daily_data_sum_1.json  |     4 +-
 ...GLONASS_Daily_Antenna_Phase_Centers_1.json |     4 +-
 ...LONASS_Daily_Antenna_Phase_Map_Meta_1.json |     4 +-
 .../CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json    |     4 +-
 ..._GD_GLONASS_Daily_POD_1sec_clk_corr_1.json |     4 +-
 ...ily_POD_30sec_Attitude_Quarternions_1.json |     4 +-
 ...S_GD_GLONASS_Daily_POD_60sec_Orbits_1.json |     4 +-
 ...GD_GLONASS_Daily_POD_60sec_clk_corr_1.json |     4 +-
 ...ily_POD_30sec_Attitude_Quarternions_1.json |     4 +-
 ...Galileo_Daily_Antenna_Phase_Centers_1.json |     4 +-
 ...alileo_Daily_Antenna_Phase_Map_Meta_1.json |     4 +-
 .../CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json    |     4 +-
 ..._GD_Galileo_Daily_POD_1sec_clk_corr_1.json |     4 +-
 ...ily_POD_30sec_Attitude_Quarternions_1.json |     4 +-
 ...D_Galileo_Daily_POD_60-sec_clk_corr_1.json |     4 +-
 ...S_GD_Galileo_Daily_POD_60sec_Orbits_1.json |     4 +-
 ...DDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json |     4 +-
 ...NASS_POD_30sec_Attitude_Quaternions_1.json |     4 +-
 ...DIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json |     4 +-
 ...CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json |     4 +-
 .../CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json   |     4 +-
 ..._GPS_POD_30sec_Attitude_Quaternions_1.json |     4 +-
 .../CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json  |     4 +-
 .../CDDIS_GNSS_GPS_POD_60sec_orbits_1.json    |     4 +-
 ...DDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json |     4 +-
 ...ileo_POD_30sec_Attitude_Quaternions_1.json |     4 +-
 ...DIS_GNSS_Galileo_POD_60sec_clk_corr_1.json |     4 +-
 ...CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json |     4 +-
 datasets/CDDIS_GNSS_IGSDCB_product_1.json     |     4 +-
 datasets/CDDIS_GNSS_daily_data_1.json         |     4 +-
 .../CDDIS_GNSS_daily_data_combinednav_1.json  |     4 +-
 .../CDDIS_GNSS_daily_data_compactobs_1.json   |     4 +-
 .../CDDIS_GNSS_daily_data_galileonav_1.json   |     4 +-
 .../CDDIS_GNSS_daily_data_glonassnav_1.json   |     4 +-
 datasets/CDDIS_GNSS_daily_data_gpsnav_1.json  |     4 +-
 datasets/CDDIS_GNSS_daily_data_met_1.json     |     4 +-
 .../CDDIS_GNSS_daily_data_mixednav_1.json     |     4 +-
 datasets/CDDIS_GNSS_daily_data_sum_1.json     |     4 +-
 ...CDDIS_GNSS_highrate_data_compactobs_1.json |     4 +-
 ...CDDIS_GNSS_highrate_data_galileonav_1.json |     4 +-
 ...CDDIS_GNSS_highrate_data_glonassnav_1.json |     4 +-
 .../CDDIS_GNSS_highrate_data_gpsnav_1.json    |     4 +-
 datasets/CDDIS_GNSS_highrate_data_met_1.json  |     4 +-
 .../CDDIS_GNSS_highrate_data_mixednav_1.json  |     4 +-
 datasets/CDDIS_GNSS_hourly_data_1.json        |     4 +-
 .../CDDIS_GNSS_hourly_data_combinednav_1.json |     4 +-
 .../CDDIS_GNSS_hourly_data_compactobs_1.json  |     4 +-
 .../CDDIS_GNSS_hourly_data_galileonav_1.json  |     4 +-
 .../CDDIS_GNSS_hourly_data_glonassnav_1.json  |     4 +-
 datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json |     4 +-
 datasets/CDDIS_GNSS_hourly_data_met_1.json    |     4 +-
 datasets/CDDIS_GNSS_realtime_data_1.json      |     4 +-
 datasets/CDDIS_SLR_data_daily_fr_1.json       |     4 +-
 datasets/CDDIS_SLR_data_daily_npt_1.json      |     4 +-
 datasets/CDDIS_SLR_data_hourly_npt_1.json     |     4 +-
 datasets/CDDIS_SLR_data_monthly_fr_1.json     |     4 +-
 datasets/CHELTON_SEASAT_SASS_L3_1.json        |     4 +-
 .../CIESIN_CHRR_NDH_VOLCANO_HFD_1.00.json     |     4 +-
 .../CIESIN_CHRR_NDH_VOLCANO_MRD_1.00.json     |     4 +-
 .../CIESIN_CHRR_NDH_VOLCANO_PELRD_1.00.json   |     4 +-
 .../CIESIN_CHRR_NDH_VOLCANO_TELRD_1.00.json   |     4 +-
 datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json   |   130 +
 .../CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json    |     4 +-
 .../CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json   |     4 +-
 .../CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json     |     4 +-
 .../CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json    |     4 +-
 .../CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json  |     2 +-
 .../CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json |     4 +-
 datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json |     4 +-
 datasets/CIESIN_SEDAC_GRUMPv1_EXT_1.00.json   |     4 +-
 datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json |    10 +-
 .../CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json  |    14 +-
 .../CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json    |    14 +-
 .../CIESIN_SEDAC_GRUMPv1_POPCOUNT_1.00.json   |     4 +-
 .../CIESIN_SEDAC_GRUMPv1_POPDENS_1.00.json    |    14 +-
 .../CIESIN_SEDAC_GRUMPv1_STLMNT01_1.01.json   |     4 +-
 .../CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json     |     4 +-
 datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json |     4 +-
 ...SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json |     4 +-
 datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json  |     4 +-
 datasets/CIESIN_SEDAC_SDEI_UHI2013_1.00.json  |     4 +-
 datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json  |    10 +-
 datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json   |    10 +-
 datasets/CIESIN_SEDAC_USCG_SF1_1.00.json      |    10 +-
 datasets/CIESIN_SEDAC_USCG_SF3_1.00.json      |    10 +-
 .../CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json     |     4 +-
 datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json  |     4 +-
 datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json  |     4 +-
 datasets/CYGNSS_L1_CAL_RAW_IF_V1.0_1.0.json   |     4 +-
 datasets/CYGNSS_L1_CDR_V1.0_1.0.json          |     4 +-
 datasets/CYGNSS_L1_CDR_V1.1_1.1.json          |     4 +-
 datasets/CYGNSS_L1_CDR_V1.2_1.2.json          |     4 +-
 datasets/CYGNSS_L1_FULL_DDM_1.0.json          |     4 +-
 datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json     |     4 +-
 datasets/CYGNSS_L1_RAW_IF_1.0.json            |     4 +-
 datasets/CYGNSS_L1_V2.1_2.1.json              |     4 +-
 datasets/CYGNSS_L1_V3.0_3.0.json              |     4 +-
 datasets/CYGNSS_L1_V3.1_3.1.json              |     4 +-
 datasets/CYGNSS_L1_V3.2_3.2.json              |     4 +-
 datasets/CYGNSS_L2_CDR_V1.0_1.0.json          |     4 +-
 datasets/CYGNSS_L2_CDR_V1.1_1.1.json          |     4 +-
 datasets/CYGNSS_L2_CDR_V1.2_1.2.json          |     4 +-
 .../CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json  |     4 +-
 .../CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json  |     4 +-
 .../CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json  |     4 +-
 datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json |     4 +-
 datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json |     4 +-
 datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json |     4 +-
 datasets/CYGNSS_L2_V2.1_2.1.json              |     4 +-
 datasets/CYGNSS_L2_V3.0_3.0.json              |     4 +-
 datasets/CYGNSS_L2_V3.1_3.1.json              |     4 +-
 datasets/CYGNSS_L2_V3.2_3.2.json              |     4 +-
 datasets/CYGNSS_L3_CDR_V1.0_1.0.json          |     4 +-
 datasets/CYGNSS_L3_CDR_V1.1_1.1.json          |     4 +-
 datasets/CYGNSS_L3_CDR_V1.2_1.2.json          |     4 +-
 datasets/CYGNSS_L3_MICROPLASTIC_V1.0_1.0.json |     4 +-
 datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json |     4 +-
 datasets/CYGNSS_L3_MRG_NRT_V3.2.1_3.2.1.json  |     4 +-
 datasets/CYGNSS_L3_MRG_NRT_V3.2_3.2.json      |     4 +-
 datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json      |     4 +-
 datasets/CYGNSS_L3_MRG_V3.2_3.2.json          |     4 +-
 datasets/CYGNSS_L3_S1.0_1.0.json              |     4 +-
 .../CYGNSS_L3_SOIL_MOISTURE_V1.0_1.0.json     |     4 +-
 .../CYGNSS_L3_SOIL_MOISTURE_V3.2_3.2.json     |     4 +-
 ..._UC_BERKELEY_WATERMASK_DAILY_V3.2_3.2.json |     4 +-
 ...NSS_L3_UC_BERKELEY_WATERMASK_V3.1_3.1.json |     4 +-
 datasets/CYGNSS_L3_V2.1_2.1.json              |     4 +-
 datasets/CYGNSS_L3_V3.0_3.0.json              |     4 +-
 datasets/CYGNSS_L3_V3.1_3.1.json              |     4 +-
 datasets/CYGNSS_L3_V3.2_3.2.json              |     4 +-
 .../CYGNSS_NOAA_L2_SWSP_25KM_V1.1_1.1.json    |     4 +-
 .../CYGNSS_NOAA_L2_SWSP_25KM_V1.2_1.2.json    |     4 +-
 datasets/DMI_OI-DMI-L4-GLOB-v1.0_1.0.json     |     4 +-
 .../ECCO_L4_ANCILLARY_DATA_V4R4_V4r4.json     |     4 +-
 ...CO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ..._L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...ATM_STATE_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...M_STATE_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 .../ECCO_L4_BOLUS_05DEG_DAILY_V4R4_V4r4.json  |     4 +-
 ...ECCO_L4_BOLUS_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ..._L4_BOLUS_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...4_BOLUS_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...ENS_STRAT_PRESS_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ...S_STRAT_PRESS_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...RAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...T_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...O_L4_FRESH_FLUX_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ...L4_FRESH_FLUX_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...RESH_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...SH_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 .../ECCO_L4_GEOMETRY_05DEG_V4R4_V4r4.json     |     4 +-
 ...CCO_L4_GEOMETRY_LLC0090GRID_V4R4_V4r4.json |     4 +-
 ...GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json |     4 +-
 ...4_GMAP_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json |     4 +-
 ...O_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json |     4 +-
 ...L4_GMSL_TIME_SERIES_MONTHLY_V4R4_V4r4.json |     4 +-
 ...CO_L4_HEAT_FLUX_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ..._L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...AT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...XED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ...D_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...YER_DEPTH_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...R_DEPTH_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 .../ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json  |     4 +-
 .../ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json    |     4 +-
 ...ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json |     4 +-
 .../ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json  |     4 +-
 ..._L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json |     4 +-
 ...CO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json |     4 +-
 ..._L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json |     4 +-
 ...4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json |     4 +-
 ...N_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json |     4 +-
 ...NTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...UM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...NITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...TY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...TURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...RE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...LUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...ME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...MFUNCTION_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...UNCTION_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...CO_L4_OCEAN_VEL_05DEG_DAILY_V4R4_V4r4.json |     4 +-
 ..._L4_OCEAN_VEL_05DEG_MONTHLY_V4R4_V4r4.json |     4 +-
 ...OCEAN_VEL_LLC0090GRID_DAILY_V4R4_V4r4.json |     4 +-
 ...EAN_VEL_LLC0090GRID_MONTHLY_V4R4_V4r4.json |     4 +-
 ...CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json |     4 +-
 ...O_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json |     4 +-
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 datasets/VNP43MA3_001.json                    |     4 +-
 datasets/VNP43MA4_001.json                    |     4 +-
 datasets/VNP64A1_001.json                     |     4 +-
 datasets/VNP64A1_002.json                     |    12 +-
 datasets/WAF_DEALIASED_SASS_L2_1.json         |     4 +-
 datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json        |     4 +-
 datasets/WENTZ_SASS_SIGMA0_L2_1.json          |     4 +-
 datasets/WUS_UCLA_SR_1.json                   |     4 +-
 ...alance_Monthly_Historical_GRIDMET_1.5.json |     4 +-
 .../Western USA Live Fuel Moisture_1.json     |     4 +-
 .../WindSat-REMSS-L3U-v7.0.1a_7.0.1a.json     |     4 +-
 ...ng Footprint Dataset - Accra, Ghana_1.json |     4 +-
 ...rint Dataset - Barishal, Bangladesh_1.json |     4 +-
 ...print Dataset - Bentiu, South Sudan_1.json |     4 +-
 ...nt Dataset - Chittagong, Bangladesh_1.json |     4 +-
 ...t Dataset - Cox's Bazar, Bangladesh_1.json |     4 +-
 ...t Dataset - Dar es Salaam, Tanzania_1.json |     4 +-
 ...otprint Dataset - Dhaka, Bangladesh_1.json |     4 +-
 ...Footprint Dataset - Hpa-an, Myanmar_1.json |     4 +-
 ...print Dataset - Jashore, Bangladesh_1.json |     4 +-
 ...ootprint Dataset - Karnataka, India_1.json |     4 +-
 ...ootprint Dataset - Les Cayes, Haiti_1.json |     4 +-
 ...i, Democratic Republic of the Congo_1.json |     4 +-
 ...int Dataset - Manjama, Sierra Leone_1.json |     4 +-
 ... Dataset - Mesopotamia, St. Vincent_1.json |     4 +-
 ...ng Footprint Dataset - Muscat, Oman_1.json |     4 +-
 ...g Footprint Dataset - Mzuzu, Malawi_1.json |     4 +-
 ...Footprint Dataset - N'Djamena, Chad_1.json |     4 +-
 ... Footprint Dataset - Nairobi, Kenya_1.json |     4 +-
 ...g Footprint Dataset - Paris, France_1.json |     4 +-
 ...Footprint Dataset - Shanghai, China_1.json |     4 +-
 ...tprint Dataset - Sylhet, Bangladesh_1.json |     4 +-
 ...lding Footprint Dataset - Wa, Ghana_1.json |     4 +-
 nasa_cmr_catalog.json                         | 13514 ++--------------
 nasa_cmr_catalog.tsv                          |   850 +-
 1435 files changed, 5095 insertions(+), 15542 deletions(-)
 create mode 100644 datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json
 create mode 100644 datasets/SNEX_MCS_Lidar_1.json
 create mode 100644 datasets/SNEX_MCS_Lidar_Raw_1.json

diff --git a/datasets/ABI_G16-STAR-L2P-v2.70_2.70.json b/datasets/ABI_G16-STAR-L2P-v2.70_2.70.json
index de0ebc6360..fe4763dfa9 100644
--- a/datasets/ABI_G16-STAR-L2P-v2.70_2.70.json
+++ b/datasets/ABI_G16-STAR-L2P-v2.70_2.70.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/ABI_G16-STAR-L3C-v2.70_2.70.json b/datasets/ABI_G16-STAR-L3C-v2.70_2.70.json
index 95bbdb1e2c..18c908a99a 100644
--- a/datasets/ABI_G16-STAR-L3C-v2.70_2.70.json
+++ b/datasets/ABI_G16-STAR-L3C-v2.70_2.70.json
@@ -42,7 +42,7 @@
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index 87d7dea08b..1f43e2d35b 100644
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index 00b3ed4321..6e704e55c6 100644
--- a/datasets/ABI_G17-STAR-L3C-v2.71_2.71.json
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index 2486c967e2..2a48c73408 100644
--- a/datasets/AHI_H08-STAR-L2P-v2.70_2.70.json
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index e366eeac9d..03ee1db85c 100644
--- a/datasets/AHI_H08-STAR-L3C-v2.70_2.70.json
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index 3607bb95dd..b0e82bc092 100644
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diff --git a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json
index 51841ca0c5..cbd79424b7 100644
--- a/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1.json
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index 8b4a1d41cb..18be873b90 100644
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+++ b/datasets/ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1.json
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index 9fc6855cd2..8b00c5010f 100644
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diff --git a/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json b/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json
index cbb3c4f66c..57be022cde 100644
--- a/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json
+++ b/datasets/AMSR2-REMSS-L2P_RT-v8.2_8.2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json b/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json
index f69d80504a..2b2255ac97 100644
--- a/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json
+++ b/datasets/AMSR2-REMSS-L3U-v8.2_8.2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AMSR2-REMSS-L3U-v8a_8a.json b/datasets/AMSR2-REMSS-L3U-v8a_8a.json
index 2ca6a8fadc..9f3ed80737 100644
--- a/datasets/AMSR2-REMSS-L3U-v8a_8a.json
+++ b/datasets/AMSR2-REMSS-L3U-v8a_8a.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json b/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json
index 97eb2397c4..7b4345b1fa 100644
--- a/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json
+++ b/datasets/AMSR2-REMSS-L3U_RT-v8.2_8.2.json
@@ -42,7 +42,7 @@
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index 5533611d58..19a3d4fc5d 100644
--- a/datasets/AMSR2-REMSS-L3U_RT-v8a_8a.json
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index c70a406074..748f9d4036 100644
--- a/datasets/AMSRE-REMSS-L2P-v7a_7a.json
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@@ -42,7 +42,7 @@
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index 3657d00618..a688c07237 100644
--- a/datasets/AMSRE-REMSS-L3U-v7a_7a.json
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index c1362fe1ae..de4c5956ee 100644
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index b3025d4293..e0f0b0b7af 100644
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index 6c435adbcc..85d3bf7e81 100644
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index 8f1179d08d..1792ab4658 100644
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index 9673765333..129fc5ac52 100644
--- a/datasets/ASCATA_ESDR_ANCILLARY_L2_V1.1_1.1.json
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index cb089cd4fd..ebd9954172 100644
--- a/datasets/ASCATA_ESDR_L2_WIND_STRESS_V1.1_1.1.json
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index 98176e190a..a7201adbec 100644
--- a/datasets/ASCATA_L2_25KM_CDR_1.0.json
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index 2acc62422c..df1abcc933 100644
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index fbb5b9aded..136c2fa9cd 100644
--- a/datasets/ASCATB-L2-25km_Operational_Near-Real-Time.json
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index 28c717db7f..c997cfef8b 100644
--- a/datasets/ASCATB-L2-Coastal_Operational_Near-Real-Time.json
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index a4e73086d0..cc2961cf39 100644
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index dd675cc294..2910a12bbe 100644
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index aaf35256d4..19454a1eac 100644
--- a/datasets/ASCATC-L2-25km_Operational_Near-Real-Time.json
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index d79029056b..95830a3877 100644
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index fd142efd7a..b2f65bfc40 100644
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index 065c9107c4..be6a09dccf 100644
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index 97d921483a..a88c583636 100644
--- a/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json
+++ b/datasets/AVHRR19_G-NAVO-L2P-v1.0_1.json
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index 9851383910..90252531b6 100644
--- a/datasets/AVHRR19_L-NAVO-L2P-v1.0_1.json
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diff --git a/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json b/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json
index df46d42197..5f01a58ac8 100644
--- a/datasets/AVHRRF_MA-STAR-L2P-v2.80_2.80.json
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index 9e4936add3..83b44bc7ec 100644
--- a/datasets/AVHRRF_MA-STAR-L3U-v2.80_2.80.json
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index 37be2eb429..605f386521 100644
--- a/datasets/AVHRRF_MB-STAR-L2P-v2.80_2.80.json
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index 563117d08f..79984d480e 100644
--- a/datasets/AVHRRF_MB-STAR-L3U-v2.80_2.80.json
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index 7c35aa6a94..136e573618 100644
--- a/datasets/AVHRRMTA_G-NAVO-L2P-v1.0_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json b/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json
index 6fb5bdf7ec..8d1f26f09f 100644
--- a/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json
+++ b/datasets/AVHRRMTA_G-NAVO-L2P-v2.0_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json b/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json
index c873f3fa26..37356dabc4 100644
--- a/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json
+++ b/datasets/AVHRRMTB_G-NAVO-L2P-v1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json b/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json
index 98a985333e..fffac09a88 100644
--- a/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json
+++ b/datasets/AVHRRMTB_G-NAVO-L2P-v2.0_2.0.json
@@ -42,7 +42,7 @@
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index b6cc865a69..1727b34f28 100644
--- a/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json
+++ b/datasets/AVHRRMTC_G-NAVO-L2P-v2.0_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json
index 02e55c5e6f..e0c2495713 100644
--- a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.0_2.0.json
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index 13fe735dd1..609508c1c7 100644
--- a/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json
+++ b/datasets/AVHRR_OI-NCEI-L4-GLOB-v2.1_2.1.json
@@ -42,7 +42,7 @@
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index af966d0f50..b0affe1098 100644
--- a/datasets/AVHRR_SST_METOP_A-OSISAF-L2P-v1.0_1.json
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diff --git a/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json
index 147651f687..847e329b88 100644
--- a/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json
+++ b/datasets/AVHRR_SST_METOP_A_GLB-OSISAF-L3C-v1.0_1.json
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diff --git a/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json
index ad97ed7e4d..1e1332160e 100644
--- a/datasets/AVHRR_SST_METOP_A_NAR-OSISAF-L3C-v1.0_1.json
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diff --git a/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json b/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json
index 13d65f0ab5..17bce855a5 100644
--- a/datasets/AVHRR_SST_METOP_B-OSISAF-L2P-v1.0_1.json
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@@ -42,7 +42,7 @@
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index e6acae86a7..225f1911e6 100644
--- a/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json
+++ b/datasets/AVHRR_SST_METOP_B_GLB-OSISAF-L3C-v1.0_1.json
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index e9fa1c1b55..e5321129de 100644
--- a/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json
+++ b/datasets/AVHRR_SST_METOP_B_NAR-OSISAF-L3C-v1.0_1.json
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diff --git a/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json b/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json
index 70333e6c69..68cc4f2aa4 100644
--- a/datasets/AVHRR_SST_NOAA19_NAR-OSISAF-L3C-v1.0_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/BAROCLINIC_HRET14_14.json b/datasets/BAROCLINIC_HRET14_14.json
index 963641ae56..e321673bb6 100644
--- a/datasets/BAROCLINIC_HRET14_14.json
+++ b/datasets/BAROCLINIC_HRET14_14.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CAM5K30CF_003.json b/datasets/CAM5K30CF_003.json
index 5092ed0b96..8843e2bdfe 100644
--- a/datasets/CAM5K30CF_003.json
+++ b/datasets/CAM5K30CF_003.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CAM5K30EM_001.json b/datasets/CAM5K30EM_001.json
index e6ddc2d708..19146b2ef9 100644
--- a/datasets/CAM5K30EM_001.json
+++ b/datasets/CAM5K30EM_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CAM5K30EM_003.json b/datasets/CAM5K30EM_003.json
index 8ee0680ceb..7e66353dee 100644
--- a/datasets/CAM5K30EM_003.json
+++ b/datasets/CAM5K30EM_003.json
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diff --git a/datasets/CAM5K30UC_001.json b/datasets/CAM5K30UC_001.json
index 3f9c695ce5..72a95ccd14 100644
--- a/datasets/CAM5K30UC_001.json
+++ b/datasets/CAM5K30UC_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CAM5K30UC_003.json b/datasets/CAM5K30UC_003.json
index 62b91ef464..f21df80b09 100644
--- a/datasets/CAM5K30UC_003.json
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@@ -42,7 +42,7 @@
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index 3f58cd14bb..5d520c6132 100644
--- a/datasets/CCMP_WINDS_10M6HR_L4_V3.1_3.1.json
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index 863e058097..76147c2ae8 100644
--- a/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json
+++ b/datasets/CCMP_WINDS_10MMONTHLY_L4_V3.1_3.1.json
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diff --git a/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json b/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json
index a2fef5715c..d2be644c49 100644
--- a/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json
+++ b/datasets/CDDIS_GLONASS_daily_data_combinednav_1.json
@@ -42,7 +42,7 @@
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index e3db4212fe..0b5b16bbcc 100644
--- a/datasets/CDDIS_GLONASS_daily_data_compactobs_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GLONASS_daily_data_met_1.json b/datasets/CDDIS_GLONASS_daily_data_met_1.json
index fe6e96c6fb..592693fc90 100644
--- a/datasets/CDDIS_GLONASS_daily_data_met_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GLONASS_daily_data_obs_1.json b/datasets/CDDIS_GLONASS_daily_data_obs_1.json
index ae5fe8d731..1eca5fd625 100644
--- a/datasets/CDDIS_GLONASS_daily_data_obs_1.json
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index 0590ac0f4b..b6df4b451c 100644
--- a/datasets/CDDIS_GLONASS_daily_data_sum_1.json
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index 297ad4bdba..32c33724cd 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json
+++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Centers_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json
index b0a6f7eea4..929d605ced 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json
+++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_Antenna_Phase_Map_Meta_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json
index a9b8b3d14e..c0ed49d449 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_EOP_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json
index 5607563ae3..3d94eeb026 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_1sec_clk_corr_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json
index 2e0750a117..eef9d0b200 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_30sec_Attitude_Quarternions_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json
index b2a122deb2..78b3ad0007 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_Orbits_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json
index 16d08dd782..bef2cb4269 100644
--- a/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_GD_GLONASS_Daily_POD_60sec_clk_corr_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json
index 82f1f7b1cc..8f87d7a3fb 100644
--- a/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json
+++ b/datasets/CDDIS_GNSS_GD_GPS_Daily_POD_30sec_Attitude_Quarternions_1.json
@@ -42,7 +42,7 @@
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index 34db647d27..51f6237a65 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Centers_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json
index 5c1d8c4d0d..10648d7dcf 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_Antenna_Phase_Map_Meta_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json
index de0c59b166..bc496a19c8 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_EOP_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json
index 86999e69e2..d0e8bf52ff 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_1sec_clk_corr_1.json
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index a33628fd28..848106db88 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json
+++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_30sec_Attitude_Quarternions_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json
index e232a5fba4..dc7dae7f83 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60-sec_clk_corr_1.json
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diff --git a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json b/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json
index 6c9be3249c..e78cff01ca 100644
--- a/datasets/CDDIS_GNSS_GD_Galileo_Daily_POD_60sec_Orbits_1.json
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diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json
index 67a5644067..d606665c74 100644
--- a/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_GLONASS_POD_1sec_clk_corr_1.json
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diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json
index aee24b248a..51e830a459 100644
--- a/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json
+++ b/datasets/CDDIS_GNSS_GLONASS_POD_30sec_Attitude_Quaternions_1.json
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diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json
index 3fa7f79e78..01638e97dc 100644
--- a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_clk_corr_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json
index 7209369277..42fc499c0a 100644
--- a/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json
+++ b/datasets/CDDIS_GNSS_GLONASS_POD_60sec_orbits_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json
index 1f2b450dd5..6da93ac73f 100644
--- a/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_GPS_POD_1sec_clk_corr_1.json
@@ -42,7 +42,7 @@
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index 29453d513d..ab289664fd 100644
--- a/datasets/CDDIS_GNSS_GPS_POD_30sec_Attitude_Quaternions_1.json
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diff --git a/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json
index d28b06d334..7939d06314 100644
--- a/datasets/CDDIS_GNSS_GPS_POD_60sec_clk_corr_1.json
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diff --git a/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json b/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json
index cf6e63ca63..653a589186 100644
--- a/datasets/CDDIS_GNSS_GPS_POD_60sec_orbits_1.json
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diff --git a/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json b/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json
index a9dab063e0..f9fdbdb902 100644
--- a/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json
+++ b/datasets/CDDIS_GNSS_Galileo_POD_1sec_clk_corr_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json b/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json
index 1f7753bfcc..fda886f65e 100644
--- a/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json
+++ b/datasets/CDDIS_GNSS_Galileo_POD_30sec_Attitude_Quaternions_1.json
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diff --git a/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json b/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json
index 8cdd0da62b..8c73320f9e 100644
--- a/datasets/CDDIS_GNSS_Galileo_POD_60sec_clk_corr_1.json
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index 309232d2c6..9fa6fb8f47 100644
--- a/datasets/CDDIS_GNSS_Galileo_POD_60sec_orbits_1.json
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diff --git a/datasets/CDDIS_GNSS_IGSDCB_product_1.json b/datasets/CDDIS_GNSS_IGSDCB_product_1.json
index c6658dfe4a..3d22ceb1a1 100644
--- a/datasets/CDDIS_GNSS_IGSDCB_product_1.json
+++ b/datasets/CDDIS_GNSS_IGSDCB_product_1.json
@@ -42,7 +42,7 @@
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         {
             "rel": "self",
-            "href": "https://cmr.earthdata.nasa.gov/stac/CDDIS/collections?cursor=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_GNSS_IGSDCB_product_1",
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@@ -53,7 +53,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_1.json b/datasets/CDDIS_GNSS_daily_data_1.json
index ed92fbd75d..f56a6769d2 100644
--- a/datasets/CDDIS_GNSS_daily_data_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_combinednav_1.json b/datasets/CDDIS_GNSS_daily_data_combinednav_1.json
index f65cea9a99..1b0b639dd1 100644
--- a/datasets/CDDIS_GNSS_daily_data_combinednav_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_combinednav_1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_compactobs_1.json b/datasets/CDDIS_GNSS_daily_data_compactobs_1.json
index f3878c8417..8f52d777da 100644
--- a/datasets/CDDIS_GNSS_daily_data_compactobs_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_compactobs_1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_galileonav_1.json b/datasets/CDDIS_GNSS_daily_data_galileonav_1.json
index cb3e9d90cd..4d7093fd5e 100644
--- a/datasets/CDDIS_GNSS_daily_data_galileonav_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_galileonav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json b/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json
index c98de402eb..2c2a30f7d3 100644
--- a/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_glonassnav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json b/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json
index 4edcc02604..e405acd2c8 100644
--- a/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_gpsnav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_met_1.json b/datasets/CDDIS_GNSS_daily_data_met_1.json
index 695d3b552b..b265a0ff59 100644
--- a/datasets/CDDIS_GNSS_daily_data_met_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_met_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_mixednav_1.json b/datasets/CDDIS_GNSS_daily_data_mixednav_1.json
index e8892a5183..083bd0bc34 100644
--- a/datasets/CDDIS_GNSS_daily_data_mixednav_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_mixednav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_daily_data_sum_1.json b/datasets/CDDIS_GNSS_daily_data_sum_1.json
index 4813f3d0f3..f2fba5ad7b 100644
--- a/datasets/CDDIS_GNSS_daily_data_sum_1.json
+++ b/datasets/CDDIS_GNSS_daily_data_sum_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json b/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json
index c2adc37a37..9b6f4da9d6 100644
--- a/datasets/CDDIS_GNSS_highrate_data_compactobs_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json b/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json
index 38f5b09684..5bae0a7b50 100644
--- a/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json
+++ b/datasets/CDDIS_GNSS_highrate_data_galileonav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json b/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json
index 805ca398e4..dfb9cdca80 100644
--- a/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json
+++ b/datasets/CDDIS_GNSS_highrate_data_glonassnav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json b/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json
index a83dd25974..04d9f59168 100644
--- a/datasets/CDDIS_GNSS_highrate_data_gpsnav_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_met_1.json b/datasets/CDDIS_GNSS_highrate_data_met_1.json
index 3f77ed1279..532b0cae8b 100644
--- a/datasets/CDDIS_GNSS_highrate_data_met_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json b/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json
index 1e53e59b05..a48da3fe82 100644
--- a/datasets/CDDIS_GNSS_highrate_data_mixednav_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_1.json b/datasets/CDDIS_GNSS_hourly_data_1.json
index c4124ab8ab..b2f619f52a 100644
--- a/datasets/CDDIS_GNSS_hourly_data_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json b/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json
index b14bb04f95..d01887ebde 100644
--- a/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_combinednav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json b/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json
index 5675a89547..cf40884246 100644
--- a/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_compactobs_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json b/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json
index 1d9433c82c..171bdf0bbe 100644
--- a/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_galileonav_1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json b/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json
index 2a2719690f..fb312b3593 100644
--- a/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_glonassnav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json b/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json
index 8924d5e2f0..e5f74b43db 100644
--- a/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_gpsnav_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_hourly_data_met_1.json b/datasets/CDDIS_GNSS_hourly_data_met_1.json
index f635e49073..b8b8c8c32b 100644
--- a/datasets/CDDIS_GNSS_hourly_data_met_1.json
+++ b/datasets/CDDIS_GNSS_hourly_data_met_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_GNSS_realtime_data_1.json b/datasets/CDDIS_GNSS_realtime_data_1.json
index 48d6e2aec8..5951708e5d 100644
--- a/datasets/CDDIS_GNSS_realtime_data_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CDDIS_SLR_data_daily_fr_1.json b/datasets/CDDIS_SLR_data_daily_fr_1.json
index fcac6c3fbd..701756874c 100644
--- a/datasets/CDDIS_SLR_data_daily_fr_1.json
+++ b/datasets/CDDIS_SLR_data_daily_fr_1.json
@@ -42,7 +42,7 @@
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index b98ce89cea..21775ce3ce 100644
--- a/datasets/CDDIS_SLR_data_daily_npt_1.json
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diff --git a/datasets/CDDIS_SLR_data_hourly_npt_1.json b/datasets/CDDIS_SLR_data_hourly_npt_1.json
index 37b86b283b..f6bd0d6c02 100644
--- a/datasets/CDDIS_SLR_data_hourly_npt_1.json
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diff --git a/datasets/CDDIS_SLR_data_monthly_fr_1.json b/datasets/CDDIS_SLR_data_monthly_fr_1.json
index a81c2715ca..c33edd63ec 100644
--- a/datasets/CDDIS_SLR_data_monthly_fr_1.json
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index 9fe9be0c3b..8e392b96c1 100644
--- a/datasets/CHELTON_SEASAT_SASS_L3_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json b/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json
new file mode 100644
index 0000000000..948dde497b
--- /dev/null
+++ b/datasets/CIESIN_SEDAC_GHSL_PBSMOD_1.00.json
@@ -0,0 +1,130 @@
+{
+    "type": "Collection",
+    "id": "CIESIN_SEDAC_GHSL_PBSMOD_1.00",
+    "stac_version": "1.0.0",
+    "description": "The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.",
+    "links": [
+        {
+            "rel": "license",
+            "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy",
+            "type": "text/html",
+            "title": "EOSDIS Data Use Policy"
+        },
+        {
+            "rel": "about",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.html",
+            "type": "text/html",
+            "title": "HTML metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.native",
+            "type": "application/xml",
+            "title": "Native metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.echo10",
+            "type": "application/echo10+xml",
+            "title": "ECHO10 metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.json",
+            "type": "application/json",
+            "title": "CMR JSON metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.umm_json",
+            "type": "application/vnd.nasa.cmr.umm+json",
+            "title": "CMR UMM_JSON metadata for collection"
+        },
+        {
+            "rel": "self",
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+            "type": "application/json"
+        },
+        {
+            "rel": "root",
+            "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC",
+            "type": "application/json",
+            "title": "SEDAC STAC Catalog"
+        },
+        {
+            "rel": "items",
+            "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZ2xvYmFsIGVhcnRocXVha2UgcHJvcG9ydGlvbmFsIGVjb25vbWljIGxvc3MgcmlzayBkZWNpbGVzXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX0NIUlJfTkRIX0VRVUFLRV9QRUxSRFwiLFwiMS4wMFwiLDE3OTAwMTc3MywyOF0iLCJ1bW0iOiJbXCJnbG9iYWwgZWFydGhxdWFrZSBwcm9wb3J0aW9uYWwgZWNvbm9taWMgbG9zcyByaXNrIGRlY2lsZXNcIixcIlNFREFDXCIsXCJDSUVTSU5fQ0hSUl9OREhfRVFVQUtFX1BFTFJEXCIsXCIxLjAwXCIsMTc5MDAxNzczLDI4XSJ9/CIESIN_SEDAC_GHSL_PBSMOD_1.00/items",
+            "type": "application/json"
+        }
+    ],
+    "provider": [
+        {
+            "name": "SEDAC",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "NASA EOSDIS",
+            "roles": [
+                "host"
+            ]
+        }
+    ],
+    "title": "Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid",
+    "extent": {
+        "spatial": {
+            "bbox": [
+                [
+                    -180,
+                    -90,
+                    180,
+                    90
+                ]
+            ]
+        },
+        "temporal": {
+            "interval": [
+                [
+                    "1975-01-01T00:00:00Z",
+                    "2015-01-01T00:00:00Z"
+                ]
+            ]
+        }
+    },
+    "license": "proprietary",
+    "assets": {
+        "browse": {
+            "href": "https://sedac.ciesin.columbia.edu/data/set/ghsl-population-built-up-estimates-degree-urban-smod/maps",
+            "type": "image/jpeg",
+            "title": "Download maps",
+            "roles": [
+                "browse"
+            ]
+        },
+        "thumbnail_0": {
+            "href": "https://sedac.ciesin.columbia.edu/downloads/maps/ghsl/ghsl-population-built-up-estimates-degree-urban-smod/ghsl-population-built-up-estimates-degree-urban-smod-pop-2015-thumbnail.jpg",
+            "title": "Thumbnail [0]",
+            "description": "Sample browse graphic of the data set.",
+            "roles": [
+                "thumbnail"
+            ]
+        },
+        "thumbnail_1": {
+            "href": "https://sedac.ciesin.columbia.edu/data/set/ghsl-population-built-up-estimates-degree-urban-smod/maps",
+            "title": "Thumbnail [1]",
+            "description": "Maps Download Page",
+            "roles": [
+                "thumbnail"
+            ]
+        },
+        "nasa": {
+            "href": "https://search.earthdata.nasa.gov/search/granules?p=C179001779-SEDAC",
+            "title": "Direct Download",
+            "description": "Data Download Page",
+            "roles": [
+                "data"
+            ]
+        }
+    }
+}
\ No newline at end of file
diff --git a/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json
index c430776583..c9d9126a0f 100644
--- a/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json
+++ b/datasets/CIESIN_SEDAC_GPWv3_CENTROIDS_3.00.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json
index 840216b99c..9b968e5595 100644
--- a/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json
+++ b/datasets/CIESIN_SEDAC_GPWv3_COASTLINES_3.00.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json b/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json
index 54e1337801..b8f3ef67cb 100644
--- a/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json
+++ b/datasets/CIESIN_SEDAC_GPWv3_LANDGEOG_3.00.json
@@ -42,7 +42,7 @@
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index 37bf5444ec..bf4bc3a08f 100644
--- a/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json
+++ b/datasets/CIESIN_SEDAC_GPWv3_NADMINBND_3.00.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json b/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json
index 0ac2d6e533..6ced17076c 100644
--- a/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json
+++ b/datasets/CIESIN_SEDAC_GPWv4_ADUCPPE_R11_4.11.json
@@ -128,7 +128,7 @@
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-            "title": "Direct Download [2]",
+            "title": "Direct Download [1]",
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diff --git a/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json b/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json
index 303d6379d1..529fb0eb8b 100644
--- a/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json
+++ b/datasets/CIESIN_SEDAC_GRANDv1_RESERVOIRS_1.01.json
@@ -118,8 +118,8 @@
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index f81c30bd6d..585d84c606 100644
--- a/datasets/CIESIN_SEDAC_GRUMPv1_EXT02_1.02.json
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@@ -42,7 +42,7 @@
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index 482d823dce..11736a5918 100644
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json b/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json
index 982e457277..2f71cc5c09 100644
--- a/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json
+++ b/datasets/CIESIN_SEDAC_GRUMPv1_LGUAG_1.00.json
@@ -120,15 +120,7 @@
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-        "columbia": {
-            "href": "https://sedac.ciesin.columbia.edu/gpw/app/?file=grumpv1&data=area",
-            "title": "Direct Download [1]",
+            "title": "Direct Download",
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index 07ee159f42..8a92d19499 100644
--- a/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json
+++ b/datasets/CIESIN_SEDAC_GRUMPv1_NADMINBND_1.00.json
@@ -42,7 +42,7 @@
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index ab7259999f..f01d84fd68 100644
--- a/datasets/CIESIN_SEDAC_GRUMPv1_NATIDEN_1.00.json
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index 5b94ff81e7..82dffae171 100644
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@@ -42,7 +42,7 @@
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index b9756411a3..1000ede812 100644
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index e718847cfd..5e24d8226d 100644
--- a/datasets/CIESIN_SEDAC_GRUMPv1_STLMNT_1.00.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json b/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json
index dbe1f3698b..986aaaec59 100644
--- a/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json
+++ b/datasets/CIESIN_SEDAC_PMP_IMR_V2.01_2.01.json
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diff --git a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json
index c1eeb1793a..06bff0f760 100644
--- a/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json
+++ b/datasets/CIESIN_SEDAC_SDEI_GWRPM25_MMSVAOD_5GL04_5.04.json
@@ -118,8 +118,8 @@
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diff --git a/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json b/datasets/CIESIN_SEDAC_SDEI_LST2013_1.00.json
index a0c737537b..7159f78b76 100644
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index b9627f2161..ccf048ddff 100644
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index 8c6766eb73..75ef513454 100644
--- a/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json
+++ b/datasets/CIESIN_SEDAC_USCG_SF12010_1.00.json
@@ -120,7 +120,15 @@
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index a6615768e3..86686f26be 100644
--- a/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json
+++ b/datasets/CIESIN_SEDAC_USCG_SF1MSA_1.00.json
@@ -120,7 +120,15 @@
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+            "title": "Direct Download [1]",
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index aad94ca0f4..a7fbada21b 100644
--- a/datasets/CIESIN_SEDAC_USCG_SF1_1.00.json
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@@ -120,7 +120,15 @@
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+            "title": "Direct Download [0]",
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index d3e289be84..099db79b2e 100644
--- a/datasets/CIESIN_SEDAC_USCG_SF3_1.00.json
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@@ -120,7 +120,15 @@
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+            "title": "Direct Download [0]",
+            "description": "Data Download Page",
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+            "title": "Direct Download [1]",
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index 421f06f94c..8ded6f679c 100644
--- a/datasets/CIESIN_SEDAC_USPAT_GUPPD_V1_1.00.json
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@@ -42,7 +42,7 @@
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index e19227e193..26c927d4ce 100644
--- a/datasets/CMC0.1deg-CMC-L4-GLOB-v3.0_3.0.json
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index 11fe622955..a4d13b9301 100644
--- a/datasets/CMC0.2deg-CMC-L4-GLOB-v2.0_2.0.json
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index fc4002cc1c..83709c469b 100644
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diff --git a/datasets/CYGNSS_L1_CDR_V1.0_1.0.json b/datasets/CYGNSS_L1_CDR_V1.0_1.0.json
index 20b498d4ad..a94bd2a011 100644
--- a/datasets/CYGNSS_L1_CDR_V1.0_1.0.json
+++ b/datasets/CYGNSS_L1_CDR_V1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_CDR_V1.1_1.1.json b/datasets/CYGNSS_L1_CDR_V1.1_1.1.json
index b2fb73d73d..ade65d00fd 100644
--- a/datasets/CYGNSS_L1_CDR_V1.1_1.1.json
+++ b/datasets/CYGNSS_L1_CDR_V1.1_1.1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_CDR_V1.2_1.2.json b/datasets/CYGNSS_L1_CDR_V1.2_1.2.json
index bc2c108ebf..c2c422f1fa 100644
--- a/datasets/CYGNSS_L1_CDR_V1.2_1.2.json
+++ b/datasets/CYGNSS_L1_CDR_V1.2_1.2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_FULL_DDM_1.0.json b/datasets/CYGNSS_L1_FULL_DDM_1.0.json
index a5d675ef9b..d70d6578bf 100644
--- a/datasets/CYGNSS_L1_FULL_DDM_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json b/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json
index 565a96daf9..e7a0990fcb 100644
--- a/datasets/CYGNSS_L1_FULL_DDM_V3.0_3.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_RAW_IF_1.0.json b/datasets/CYGNSS_L1_RAW_IF_1.0.json
index 6322fe60c9..2c78315e65 100644
--- a/datasets/CYGNSS_L1_RAW_IF_1.0.json
+++ b/datasets/CYGNSS_L1_RAW_IF_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_V2.1_2.1.json b/datasets/CYGNSS_L1_V2.1_2.1.json
index 12cafd382f..03297be4ba 100644
--- a/datasets/CYGNSS_L1_V2.1_2.1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_V3.0_3.0.json b/datasets/CYGNSS_L1_V3.0_3.0.json
index e83f75745b..f25566fcc7 100644
--- a/datasets/CYGNSS_L1_V3.0_3.0.json
+++ b/datasets/CYGNSS_L1_V3.0_3.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_V3.1_3.1.json b/datasets/CYGNSS_L1_V3.1_3.1.json
index aa9d1ff185..bb65544341 100644
--- a/datasets/CYGNSS_L1_V3.1_3.1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L1_V3.2_3.2.json b/datasets/CYGNSS_L1_V3.2_3.2.json
index 81774c9127..d7780cce51 100644
--- a/datasets/CYGNSS_L1_V3.2_3.2.json
+++ b/datasets/CYGNSS_L1_V3.2_3.2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_CDR_V1.0_1.0.json b/datasets/CYGNSS_L2_CDR_V1.0_1.0.json
index 6da7d12b57..7af1667065 100644
--- a/datasets/CYGNSS_L2_CDR_V1.0_1.0.json
+++ b/datasets/CYGNSS_L2_CDR_V1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_CDR_V1.1_1.1.json b/datasets/CYGNSS_L2_CDR_V1.1_1.1.json
index e63c9d0850..1a4f290efd 100644
--- a/datasets/CYGNSS_L2_CDR_V1.1_1.1.json
+++ b/datasets/CYGNSS_L2_CDR_V1.1_1.1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_CDR_V1.2_1.2.json b/datasets/CYGNSS_L2_CDR_V1.2_1.2.json
index 33bc42cc1c..9522a99a46 100644
--- a/datasets/CYGNSS_L2_CDR_V1.2_1.2.json
+++ b/datasets/CYGNSS_L2_CDR_V1.2_1.2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json
index 1398dd3477..4e24d78c7d 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json
+++ b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json
index 7390eb26da..aa758e1b84 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json
+++ b/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.1_1.1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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index 1f84364a3c..4ece8632c1 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_CDR_V1.2_1.2.json
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index 7c08830469..3756b888fe 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_V1.0_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json b/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json
index 1b3ec25345..254bc3109b 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_V2.0_2.0.json
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diff --git a/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json b/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json
index a94e9c0ea3..ab541fa80f 100644
--- a/datasets/CYGNSS_L2_SURFACE_FLUX_V3.2_3.2.json
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diff --git a/datasets/CYGNSS_L2_V2.1_2.1.json b/datasets/CYGNSS_L2_V2.1_2.1.json
index cbf6b0e943..0dd0a27b77 100644
--- a/datasets/CYGNSS_L2_V2.1_2.1.json
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@@ -42,7 +42,7 @@
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index 3364d16a75..a733292b8d 100644
--- a/datasets/CYGNSS_L2_V3.0_3.0.json
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index 74058d4133..4b95041238 100644
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index f9fcd78e01..6385cd2572 100644
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index bd9334f703..7ae8ec6f4f 100644
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index a63c9fd0e1..49581f51c2 100644
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index 473de833e0..47b190ca17 100644
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index 271d201836..d575661829 100644
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index 4f7ca5952f..f1dbce293f 100644
--- a/datasets/CYGNSS_L3_MICROPLASTIC_V3.2_3.2.json
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index 13f026c6ae..f30b00200a 100644
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index a1ddbd8ed5..eb8d94b149 100644
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diff --git a/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json b/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json
index 047dac9f07..d6b27b0f53 100644
--- a/datasets/CYGNSS_L3_MRG_V3.2.1_3.2.1.json
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@@ -42,7 +42,7 @@
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index c1a9b9aab9..716304a3a8 100644
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index 6e3067b335..617da32ed9 100644
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index f7e9a7f6f4..3db2649c40 100644
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index baab459ae7..02673c2776 100644
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index 8229d11437..f10d1dac6d 100644
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index e4b4aa8ed8..b31f617973 100644
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index da034f33cd..fa42d55e2b 100644
--- a/datasets/CYGNSS_L3_V2.1_2.1.json
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index bb96fa4b86..38ceb302e2 100644
--- a/datasets/CYGNSS_L3_V3.0_3.0.json
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index 8941dd3d4f..1ebda4507c 100644
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diff --git a/datasets/CYGNSS_L3_V3.2_3.2.json b/datasets/CYGNSS_L3_V3.2_3.2.json
index ac06676c7a..15cbf9855a 100644
--- a/datasets/CYGNSS_L3_V3.2_3.2.json
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@@ -42,7 +42,7 @@
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index fd3f33a88d..3adf9aacb7 100644
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index fc43a5727c..c4aa4e73e5 100644
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index 438df27400..85fd05c2f7 100644
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index 3bf7eace1c..850681f6ae 100644
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index cd555c0ac1..93e48b7b9d 100644
--- a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 73ef1e7aa6..08455e39f8 100644
--- a/datasets/ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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index a918a6d680..4f9a75692b 100644
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index bd82e66c08..f4fc038d23 100644
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index 5866cbf16f..37019dfb78 100644
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index 0e9ca91d37..37daed9ab3 100644
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index 334be2d0f3..7154a685c3 100644
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index 222dde7951..00a8508f9e 100644
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index a29bca5cbb..9bb141c869 100644
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+++ b/datasets/ECCO_L4_GMAP_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json
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index 8bf7867df0..a455173eb3 100644
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diff --git a/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json
index bd0eca384f..fb3c224065 100644
--- a/datasets/ECCO_L4_GMSL_TIME_SERIES_DAILY_V4R4_V4r4.json
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index 82e978f437..727ded9bab 100644
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index 2ce175fb93..e479f8ab4e 100644
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diff --git a/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json
index 28abd5b051..3513505453 100644
--- a/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_HEAT_FLUX_05DEG_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index 509789bb3d..c87d2617a5 100644
--- a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index a36b4b67d0..393283ee89 100644
--- a/datasets/ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json
index a2a9182b81..7fca638b1e 100644
--- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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index e416d2bce8..66d32350a0 100644
--- a/datasets/ECCO_L4_MIXED_LAYER_DEPTH_05DEG_MONTHLY_V4R4_V4r4.json
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index 7e1f96c69e..e4eb94889a 100644
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index 397e16e8ce..d619899a6e 100644
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diff --git a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4B_V4r4b.json
index f4c564d5ae..8628c5e730 100644
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@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json
index 4d0203b251..b4b2781ca8 100644
--- a/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OBP_05DEG_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json
index c89e1723b2..cfe29e9917 100644
--- a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json
+++ b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4B_V4r4b.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json
index c0b9cca158..b8f00640d5 100644
--- a/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OBP_05DEG_MONTHLY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json
index ea14df9364..8fe56272d5 100644
--- a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json
+++ b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4B_V4r4b.json
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diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json
index ff94a98aa0..38dbe18dd5 100644
--- a/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OBP_LLC0090GRID_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json
index 91a0e06dd4..d1bfaa08cb 100644
--- a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json
+++ b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json
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diff --git a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 9cbe5a7a27..d34f3f8c03 100644
--- a/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OBP_LLC0090GRID_MONTHLY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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index 9cabb03c3e..a123244f9f 100644
--- a/datasets/ECCO_L4_OBP_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json
index 4df1ba2065..6ac93e3665 100644
--- a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_05DEG_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json
index b273947c15..df55a34a2c 100644
--- a/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_MIX_COEFFS_LLC0090GRID_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json
index acd3d4e73f..aa56218299 100644
--- a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 57baf7e893..16ec747b5b 100644
--- a/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_MOMENTUM_TEND_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index e603c16850..80aa57886e 100644
--- a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
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index a1c3e0214d..58ffb0c0d2 100644
--- a/datasets/ECCO_L4_OCEAN_3D_SALINITY_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index 6fd6b80ce2..de618f62b9 100644
--- a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 437041f129..d5eaab31da 100644
--- a/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index 5270fc719d..0ce10a7c85 100644
--- a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index d622653451..1e88fb2e56 100644
--- a/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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index eaf2da91ea..c0b475dc27 100644
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index cde578458e..ad521d3489 100644
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index 344c3ccd66..ec0040ff76 100644
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index 355ac3537a..8d2fb641c2 100644
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index 3999e5148f..aa357521df 100644
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diff --git a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4B_V4r4b.json
index 901b290ac0..c2631ae819 100644
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diff --git a/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SBO_CORE_TIME_SERIES_SNAPSHOT_V4R4_V4r4.json
index b02a3b9393..2feda65cdf 100644
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index 17fbe53eea..e7ac75e6e0 100644
--- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_05DEG_MONTHLY_V4R4_V4r4.json
index 9c35ee4219..7e01deb2bd 100644
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index b5852bf124..6a6cc0a7ab 100644
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diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 386dda1e7a..3ae2c65ed8 100644
--- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
index 4c16e415ba..ad38eb5b43 100644
--- a/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index 3544075a27..29f6199b83 100644
--- a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index b005351ff5..487c8d5d9d 100644
--- a/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
index bd4afc1da2..220e321bb6 100644
--- a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_DAILY_V4R4_V4r4.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index e300b62931..579dd68fc2 100644
--- a/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json
index 4cdf1c3954..2faaa42960 100644
--- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json
index c9f9ec00e4..789c9895ec 100644
--- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_05DEG_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json
index 22f464ae94..cda50d2299 100644
--- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 14c55c16f4..5013d2d3f2 100644
--- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json b/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
index 2c574fe5ba..9a8b41700e 100644
--- a/datasets/ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_SNAPSHOT_V4R4_V4r4.json
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index bf858376bf..776e338cc4 100644
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index 488b65a93d..27f9c8cb6a 100644
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index 5a8a5a3d7f..b893fd2bb0 100644
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index e003943294..ee566bab24 100644
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+++ b/datasets/ECCO_L4_SSH_05DEG_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json
index 2194b04b44..59476cba09 100644
--- a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json
+++ b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4B_V4r4b.json
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diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json
index f32dc4a0be..e1419e8a26 100644
--- a/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_SSH_LLC0090GRID_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json
index c4b894a5fb..09122953d8 100644
--- a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4B_V4r4b.json
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diff --git a/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4_V4r4.json
index 2e29b748c9..3c535bb1a3 100644
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index 66ed2dc674..c3e180cbc5 100644
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index 98fd7a6700..d3880172ab 100644
--- a/datasets/ECCO_L4_STRESS_05DEG_DAILY_V4R4_V4r4.json
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index 9af7ec3003..ba13025380 100644
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diff --git a/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json
index 2a070f8989..5f7536e96c 100644
--- a/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_STRESS_LLC0090GRID_DAILY_V4R4_V4r4.json
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index 63a128292e..b0abdad12f 100644
--- a/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
+++ b/datasets/ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json
index 7367b34def..065d0a73c7 100644
--- a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_DAILY_V4R4_V4r4.json
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diff --git a/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json
index 0a3805b6d4..3c6faa6bb7 100644
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+++ b/datasets/ECCO_L4_TEMP_SALINITY_05DEG_MONTHLY_V4R4_V4r4.json
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index 5e838355e7..9f20948c4c 100644
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index 145d996fe4..1e2c00d1fa 100644
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index abf4a32b0d..17dfadf45e 100644
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index c52ed2a8fb..62d18df8e8 100644
--- a/datasets/ECO1BATT_001.json
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diff --git a/datasets/ECO1BGEO_001.json b/datasets/ECO1BGEO_001.json
index 9650b5e10e..1f11de3193 100644
--- a/datasets/ECO1BGEO_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO1BMAPRAD_001.json b/datasets/ECO1BMAPRAD_001.json
index 30cae8b03c..d95fa0f783 100644
--- a/datasets/ECO1BMAPRAD_001.json
+++ b/datasets/ECO1BMAPRAD_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO1BRAD_001.json b/datasets/ECO1BRAD_001.json
index 6363516cc9..0395371e0e 100644
--- a/datasets/ECO1BRAD_001.json
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diff --git a/datasets/ECO2CLD_001.json b/datasets/ECO2CLD_001.json
index 33303698a9..314e1ff1e1 100644
--- a/datasets/ECO2CLD_001.json
+++ b/datasets/ECO2CLD_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO2LSTE_001.json b/datasets/ECO2LSTE_001.json
index e51b3757e2..28f9a1941d 100644
--- a/datasets/ECO2LSTE_001.json
+++ b/datasets/ECO2LSTE_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO3ANCQA_001.json b/datasets/ECO3ANCQA_001.json
index e82c16a87a..f1d6c2e3db 100644
--- a/datasets/ECO3ANCQA_001.json
+++ b/datasets/ECO3ANCQA_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO3ETALEXIU_001.json b/datasets/ECO3ETALEXIU_001.json
index 43a29517e8..f39a0781f7 100644
--- a/datasets/ECO3ETALEXIU_001.json
+++ b/datasets/ECO3ETALEXIU_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO3ETALEXI_001.json b/datasets/ECO3ETALEXI_001.json
index 7781e977ed..7ced56d268 100644
--- a/datasets/ECO3ETALEXI_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO3ETPTJPL_001.json b/datasets/ECO3ETPTJPL_001.json
index 28e173dcd2..32ec7396ff 100644
--- a/datasets/ECO3ETPTJPL_001.json
+++ b/datasets/ECO3ETPTJPL_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/ECO4ESIALEXIU_001.json b/datasets/ECO4ESIALEXIU_001.json
index ffd8029291..7dee410f1e 100644
--- a/datasets/ECO4ESIALEXIU_001.json
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@@ -42,7 +42,7 @@
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index e5dd08b63b..55f45e3510 100644
--- a/datasets/ECO4ESIALEXI_001.json
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@@ -42,7 +42,7 @@
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index 71640a5d7b..7798b68a2e 100644
--- a/datasets/ECO4ESIPTJPL_001.json
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diff --git a/datasets/ECO4WUE_001.json b/datasets/ECO4WUE_001.json
index efcf75482e..d94417b04e 100644
--- a/datasets/ECO4WUE_001.json
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@@ -42,7 +42,7 @@
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index 1cf5598648..64e7c8720f 100644
--- a/datasets/ECO_L1B_GEO_002.json
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index 4dd303dd29..14e61fb210 100644
--- a/datasets/ECO_L1B_RAD_002.json
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index 52e29c1751..bfafc2022e 100644
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index 24d65fb091..b6d26d17bf 100644
--- a/datasets/ECO_L2T_LSTE_002.json
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index 92619da2a3..ec149fe4fe 100644
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index 91f25f7f97..54b75cc16a 100644
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index 860899060b..ca395efea4 100644
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index 9f8ff28742..82192aa9cf 100644
--- a/datasets/ECO_L3T_JET_002.json
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index 3c49051f5f..f3c3238b32 100644
--- a/datasets/ECO_L3T_MET_002.json
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index 593ced1c99..17e38be92d 100644
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index 37cbc0070a..0f4aae9157 100644
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index 0addb3f863..6c6ea5fe30 100644
--- a/datasets/ECO_L4T_ESI_002.json
+++ b/datasets/ECO_L4T_ESI_002.json
@@ -42,7 +42,7 @@
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index d2069c08f8..2a3c7c7fa0 100644
--- a/datasets/ECO_L4T_WUE_002.json
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@@ -42,7 +42,7 @@
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index 1b3ed19aca..937ab878c7 100644
--- a/datasets/EMITL1BATT_001.json
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@@ -42,7 +42,7 @@
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index 74cf95f3ca..6d07ae14e8 100644
--- a/datasets/EMITL1BRAD_001.json
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@@ -42,7 +42,7 @@
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index 1151be47a7..e1e8a8a0da 100644
--- a/datasets/EMITL2ARFL_001.json
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diff --git a/datasets/EMITL2BCH4ENH_001.json b/datasets/EMITL2BCH4ENH_001.json
index 9ea6394043..970d8c7f1b 100644
--- a/datasets/EMITL2BCH4ENH_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/EMITL2BCH4PLM_001.json b/datasets/EMITL2BCH4PLM_001.json
index 6ef2573a42..230f22c0a6 100644
--- a/datasets/EMITL2BCH4PLM_001.json
+++ b/datasets/EMITL2BCH4PLM_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/EMITL2BCO2ENH_001.json b/datasets/EMITL2BCO2ENH_001.json
index d6bc9ea359..0c17bad1c2 100644
--- a/datasets/EMITL2BCO2ENH_001.json
+++ b/datasets/EMITL2BCO2ENH_001.json
@@ -42,7 +42,7 @@
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index 4ef4f166fd..054146d05d 100644
--- a/datasets/EMITL2BCO2PLM_001.json
+++ b/datasets/EMITL2BCO2PLM_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/EMITL2BMIN_001.json b/datasets/EMITL2BMIN_001.json
index 03c19bd2da..8b9e62f2c7 100644
--- a/datasets/EMITL2BMIN_001.json
+++ b/datasets/EMITL2BMIN_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/EMITL3ASA_001.json b/datasets/EMITL3ASA_001.json
index 2f8f67a5cb..e153b65913 100644
--- a/datasets/EMITL3ASA_001.json
+++ b/datasets/EMITL3ASA_001.json
@@ -42,7 +42,7 @@
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index d2a2cd82ff..c51ca5d69f 100644
--- a/datasets/EMITL4ESM_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json b/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json
index 3ec912b3e1..402737ec81 100644
--- a/datasets/ERS-1_BYU_L3_OW_SIGMA0_ENHANCED_1.json
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diff --git a/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json b/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json
index dc7f307d07..f420c283d3 100644
--- a/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json
+++ b/datasets/ERS-2_BYU_L3_OW_SIGMA0_ENHANCED_1.json
@@ -42,7 +42,7 @@
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index 599004bf4b..d4e8f6fc6f 100644
--- a/datasets/EWSG1-NAVO-L2P-v01_1.0.json
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diff --git a/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json b/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json
index 2f420e7227..1cc2188743 100644
--- a/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json
+++ b/datasets/G18-ABI-L2P-ACSPO-v2.90_2.90.json
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index 12c1510eaa..652116a193 100644
--- a/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json
+++ b/datasets/G18-ABI-L3C-ACSPO-v2.90_2.90.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json b/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json
index 9815a5b73f..dff0f85f81 100644
--- a/datasets/GAMSSA_28km-ABOM-L4-GLOB-v01_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/GEDI01_B_002.json b/datasets/GEDI01_B_002.json
index a80b7a2c21..aa43d00619 100644
--- a/datasets/GEDI01_B_002.json
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index 489b4fa10f..62e59891ad 100644
--- a/datasets/GEDI02_A_002.json
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@@ -42,7 +42,7 @@
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index 9453171bd2..e9e2bc287c 100644
--- a/datasets/GEDI02_B_002.json
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diff --git a/datasets/GEOLST4KHR_002.json b/datasets/GEOLST4KHR_002.json
index 3b5ff63ad6..0142a089aa 100644
--- a/datasets/GEOLST4KHR_002.json
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diff --git a/datasets/GEOS-3_ALT_GDR_1.json b/datasets/GEOS-3_ALT_GDR_1.json
index 4c656bee3d..0aef51fab1 100644
--- a/datasets/GEOS-3_ALT_GDR_1.json
+++ b/datasets/GEOS-3_ALT_GDR_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GFCC30FCC_001.json b/datasets/GFCC30FCC_001.json
index 4b48c2a2b9..304bbdfba2 100644
--- a/datasets/GFCC30FCC_001.json
+++ b/datasets/GFCC30FCC_001.json
@@ -42,7 +42,7 @@
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index fc6afd340c..b4e53e8396 100644
--- a/datasets/GFCC30SR_001.json
+++ b/datasets/GFCC30SR_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GFCC30TC_003.json b/datasets/GFCC30TC_003.json
index c3f9b6acc9..457f71e699 100644
--- a/datasets/GFCC30TC_003.json
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diff --git a/datasets/GFCC30WC_001.json b/datasets/GFCC30WC_001.json
index 3d113bd52a..5b094d35c6 100644
--- a/datasets/GFCC30WC_001.json
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diff --git a/datasets/GFSAD1KCD_001.json b/datasets/GFSAD1KCD_001.json
index bf31443a69..5378fb29b3 100644
--- a/datasets/GFSAD1KCD_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/GFSAD1KCM_001.json b/datasets/GFSAD1KCM_001.json
index f0f1e93d62..1b3b1002fb 100644
--- a/datasets/GFSAD1KCM_001.json
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index a05cf84513..eaa3334eba 100644
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index a2591dcdff..2cf4ad2ded 100644
--- a/datasets/GFSAD30EUCEARUMECE_001.json
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index 81e838ee66..6fe22963fc 100644
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index 2f25c0fd66..822c5d37a2 100644
--- a/datasets/GFSAD30SACE_001.json
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index a2d8bc81e0..af3373830b 100644
--- a/datasets/GFSAD30SEACE_001.json
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index 2677ec8204..a901e280a4 100644
--- a/datasets/GFSAD30VAL_001.json
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index 999c427693..5229c8a0bc 100644
--- a/datasets/GHISACASIA_001.json
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diff --git a/datasets/GHISACONUS_001.json b/datasets/GHISACONUS_001.json
index 04614359c9..39df959900 100644
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diff --git a/datasets/GLCHMK_001.json b/datasets/GLCHMK_001.json
index 8b4b3c6d26..2fb2977738 100644
--- a/datasets/GLCHMK_001.json
+++ b/datasets/GLCHMK_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLCHMT_001.json b/datasets/GLCHMT_001.json
index c102c83edc..49df6b136b 100644
--- a/datasets/GLCHMT_001.json
+++ b/datasets/GLCHMT_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLDSMT_001.json b/datasets/GLDSMT_001.json
index fb4c23b5f1..3a5d699fbc 100644
--- a/datasets/GLDSMT_001.json
+++ b/datasets/GLDSMT_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLDTMK_001.json b/datasets/GLDTMK_001.json
index 9b89359114..a88cd6f1cd 100644
--- a/datasets/GLDTMK_001.json
+++ b/datasets/GLDTMK_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLDTMT_001.json b/datasets/GLDTMT_001.json
index 04631d5710..ba5fb20e8e 100644
--- a/datasets/GLDTMT_001.json
+++ b/datasets/GLDTMT_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLHYANC_001.json b/datasets/GLHYANC_001.json
index 5a899a2535..649cca67b4 100644
--- a/datasets/GLHYANC_001.json
+++ b/datasets/GLHYANC_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLHYVI_001.json b/datasets/GLHYVI_001.json
index fd1b56f332..b9e3ef21b5 100644
--- a/datasets/GLHYVI_001.json
+++ b/datasets/GLHYVI_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLLIDARPC_001.json b/datasets/GLLIDARPC_001.json
index 39d1ec56d6..342b72cd1a 100644
--- a/datasets/GLLIDARPC_001.json
+++ b/datasets/GLLIDARPC_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLMETRICS_001.json b/datasets/GLMETRICS_001.json
index ff6258064a..1e85e214f3 100644
--- a/datasets/GLMETRICS_001.json
+++ b/datasets/GLMETRICS_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLORTHO_001.json b/datasets/GLORTHO_001.json
index 19fca350a6..fe7cc090a7 100644
--- a/datasets/GLORTHO_001.json
+++ b/datasets/GLORTHO_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLRADS_001.json b/datasets/GLRADS_001.json
index 103aeb3366..db1661bdb1 100644
--- a/datasets/GLRADS_001.json
+++ b/datasets/GLRADS_001.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/datasets/GLREFL_001.json b/datasets/GLREFL_001.json
index 90b0987f94..12a143da40 100644
--- a/datasets/GLREFL_001.json
+++ b/datasets/GLREFL_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLTRAJECTORY_001.json b/datasets/GLTRAJECTORY_001.json
index 0aae38ea8a..cfa053d7ef 100644
--- a/datasets/GLTRAJECTORY_001.json
+++ b/datasets/GLTRAJECTORY_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GLanCE30_001.json b/datasets/GLanCE30_001.json
index cc6627528e..8801867487 100644
--- a/datasets/GLanCE30_001.json
+++ b/datasets/GLanCE30_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json b/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json
index 6fe797db41..4fceead9b4 100644
--- a/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json
+++ b/datasets/GMI-REMSS-L3U-v8.2a_8.2a.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GOES13-OSISAF-L3C-v1.0_1.json b/datasets/GOES13-OSISAF-L3C-v1.0_1.json
index 40edf7a2e9..cd7bcab3e0 100644
--- a/datasets/GOES13-OSISAF-L3C-v1.0_1.json
+++ b/datasets/GOES13-OSISAF-L3C-v1.0_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/GOES13-OSPO-L2P-v1.0_1.0.json b/datasets/GOES13-OSPO-L2P-v1.0_1.0.json
index 3772006966..80e2da0ed7 100644
--- a/datasets/GOES13-OSPO-L2P-v1.0_1.0.json
+++ b/datasets/GOES13-OSPO-L2P-v1.0_1.0.json
@@ -42,7 +42,7 @@
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index c5d8d962a8..6ec41722a8 100644
--- a/datasets/GOES15-OSPO-L2P-v1.0_1.0.json
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index 6011193438..b00cc4bff1 100644
--- a/datasets/GOES16-SST-OSISAF-L3C-v1.0_1.0.json
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index 9b104e3fcc..28252c1351 100644
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index 6e855625b9..63c12006fc 100644
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index e9fa132b07..7ba8a4002a 100644
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index f1a86ae32a..572ede6789 100644
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index 6a1afb7893..5f44d6ae20 100644
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index 12cb95270d..6ab732a975 100644
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index 714c27a633..433b7aca88 100644
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index 557b55609a..60b9aef376 100644
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index ef75624628..22dec288e1 100644
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index a4bbeaab8a..ab82e6bbe5 100644
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index 5fe1d00ad8..d6810ff422 100644
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diff --git a/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json b/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json
index b4022d2098..9ed456d79e 100644
--- a/datasets/GRACE_GAD_L2_GRAV_CSR_RL06_6.0.json
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@@ -42,7 +42,7 @@
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index 80d4fd5722..4a8389ee41 100644
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index 550854d6ab..4374bbda83 100644
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index 37c837f778..4cdb60de26 100644
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index d30e867724..f34eedecf9 100644
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index 0cd91bbae6..ef288a3e8a 100644
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index 92440abbd9..4d0adc7f62 100644
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index 041984e4b9..a0aa174523 100644
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index 4878485dde..4eb1f6b426 100644
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index 4337ee819b..1b06db80b5 100644
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diff --git a/datasets/GWELDYR_031.json b/datasets/GWELDYR_031.json
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+++ b/datasets/GWELDYR_031.json
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diff --git a/datasets/GWELDYR_032.json b/datasets/GWELDYR_032.json
index f94b1da136..6eb39e5e1f 100644
--- a/datasets/GWELDYR_032.json
+++ b/datasets/GWELDYR_032.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json b/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json
index 7d9077ed72..6b473794da 100644
--- a/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json
+++ b/datasets/Geo_Polar_Blended-OSPO-L4-GLOB-v1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json b/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json
index e757c41bcd..e1807a5773 100644
--- a/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json
+++ b/datasets/Geo_Polar_Blended_Night-OSPO-L4-GLOB-v1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json b/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json
index ab5688fe16..b2dbc9da25 100644
--- a/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json
+++ b/datasets/H09-AHI-L2P-ACSPO-v2.90_2.90.json
@@ -42,7 +42,7 @@
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index 6290286864..9492a5daeb 100644
--- a/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json
+++ b/datasets/H09-AHI-L3C-ACSPO-v2.90_2.90.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/HAWKEYE_L1_1.json b/datasets/HAWKEYE_L1_1.json
index 8a6516137e..82c13c9578 100644
--- a/datasets/HAWKEYE_L1_1.json
+++ b/datasets/HAWKEYE_L1_1.json
@@ -2,7 +2,7 @@
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     "id": "HAWKEYE_L1_1",
     "stac_version": "1.0.0",
-    "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide high quality, high resolution imagery (120 meter) of the open ocean, coastal zones, lakes, estuaries and land features. This ability provides a valuable complement to the lower resolution measurements from previous missions like SeaWiFS, MODIS and VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and is funded by the Moore Foundation under a grant for the Sustained Ocean Color Observations with Nanosatellites (SOCON).",
+    "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).",
     "links": [
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             "rel": "license",
@@ -12,54 +12,54 @@
         },
         {
             "rel": "about",
-            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3160685741-OB_CLOUD.html",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.html",
             "type": "text/html",
             "title": "HTML metadata for collection"
         },
         {
             "rel": "via",
-            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3160685741-OB_CLOUD.native",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.native",
             "type": "application/xml",
             "title": "Native metadata for collection"
         },
         {
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-            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3160685741-OB_CLOUD.echo10",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.echo10",
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-            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3160685741-OB_CLOUD.json",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.json",
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             "title": "CMR JSON metadata for collection"
         },
         {
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+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.umm_json",
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             "title": "CMR UMM_JSON metadata for collection"
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-            "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD",
+            "href": "https://cmr.earthdata.nasa.gov/stac/OB_DAAC",
             "type": "application/json",
-            "title": "OB_CLOUD STAC Catalog"
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         }
     ],
     "provider": [
         {
-            "name": "OB_CLOUD",
+            "name": "OB_DAAC",
             "roles": [
                 "producer"
             ]
@@ -71,14 +71,14 @@
             ]
         }
     ],
-    "title": "SeaHawk-1 HawkEye Level-1A  Data, version 1",
+    "title": "SeaHawk HawkEye Level-1 Data, version 1",
     "extent": {
         "spatial": {
             "bbox": [
                 [
                     -180,
-                    -90,
-                    180,
+                    90,
+                    -180,
                     90
                 ]
             ]
@@ -87,7 +87,7 @@
             "interval": [
                 [
                     "2018-12-03T00:00:00Z",
-                    "2023-10-27T00:00:00Z"
+                    null
                 ]
             ]
         }
@@ -95,30 +95,17 @@
     "license": "proprietary",
     "assets": {
         "nasa": {
-            "href": "https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-1A/SE1-Hawkeye/",
+            "href": "https://oceandata.sci.gsfc.nasa.gov/directaccess/HawkEye/L1/",
             "title": "Direct Download",
             "description": "NASA Ocean Color Web - Data Distribution Site",
             "roles": [
                 "data"
             ]
         },
-        "s3_ob_cumulus_prod_public_": {
-            "href": "s3://ob-cumulus-prod-public/",
-            "title": "ob_cumulus_prod_public_",
-            "roles": [
-                "data"
-            ]
-        },
-        "s3_credentials": {
-            "href": "https://obdaac-tea.earthdatacloud.nasa.gov/s3credentials",
-            "title": "S3 credentials API endpoint",
-            "roles": [
-                "metadata"
-            ]
-        },
-        "s3_credentials_documentation": {
-            "href": "https://obdaac-tea.earthdatacloud.nasa.gov/s3credentialsREADME",
-            "title": "S3 credentials API endpoint documentation",
+        "provider_metadata": {
+            "href": "https://oceancolor.gsfc.nasa.gov/data/10.5067/SEAHAWK/HawkEye/L1A/1",
+            "title": "Provider Metadata",
+            "description": "HawkEye-SeaHawk L1A Level-1 Dataset Landing Page",
             "roles": [
                 "metadata"
             ]
diff --git a/datasets/HLSL30_2.0.json b/datasets/HLSL30_2.0.json
index ae9553e354..6f3c32fe39 100644
--- a/datasets/HLSL30_2.0.json
+++ b/datasets/HLSL30_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/HLSS30_2.0.json b/datasets/HLSS30_2.0.json
index eee86c7c89..4beb265bce 100644
--- a/datasets/HLSS30_2.0.json
+++ b/datasets/HLSS30_2.0.json
@@ -42,7 +42,7 @@
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index c32d614bc7..2eada61892 100644
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index e0854877d5..e871f07845 100644
--- a/datasets/HOMAGE_STERIC_OHC_TIME_SERIES_v01_1.0.json
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index 0ec6094e6e..c064dbe4be 100644
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index ca70fc10f8..1afb399d75 100644
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index ef77cc4927..753eb3cf09 100644
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index 215e8240b0..d62ed9f14c 100644
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index 81812bec81..ca81da3bdd 100644
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index 424034cbe8..e25ee8ac87 100644
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index e2630ac05c..fb83d24c35 100644
--- a/datasets/JASON-1_L2_OST_GPS_E_E.json
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index a2f0514665..88838cae3e 100644
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index 72ad192e3c..5c9f01b69d 100644
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index 882a313fc6..8ac9c203af 100644
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index 8bbb08b005..ec8c0a4d0a 100644
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index 530965d248..66d0981836 100644
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index d0842f9bde..adc841044f 100644
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index 3ed5122970..69b5bc6667 100644
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index 0d05d9f7df..3cdcde253f 100644
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index 4dd178a3d6..6b83b0cc92 100644
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index 6e4030683d..d3cafb2a3c 100644
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index 091a0e6609..91df7007fb 100644
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index b9dac6b86e..2ec9741465 100644
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index cb4e5a1d23..a70e442444 100644
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index 464225207b..474f886f8e 100644
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index a6516740a9..e64db0c3cc 100644
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index e2e29bd69f..1d84de4c21 100644
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index 20f2c49f92..7d2f259c0b 100644
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index f90f6be1d0..67fd93c3c4 100644
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index f3330042d5..fe562a90ce 100644
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diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json b/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json
index 79b6933a6d..19f881c62e 100644
--- a/datasets/JASON_CS_S6A_L2_ALT_LR_RED_OST_STC_F_F.json
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index 053b150a73..434296137b 100644
--- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NRT_F_F.json
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diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json
index cb1273df81..f54ae4f185 100644
--- a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_F08.json
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diff --git a/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json b/datasets/JASON_CS_S6A_L2_ALT_LR_STD_OST_NTC_F08_UNVALIDATED_F08.json
index 7f65e1662f..4515c0d548 100644
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index 79daffb873..c42e7443e1 100644
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index 97d4c5a29e..1da8cb4465 100644
--- a/datasets/JASON_CS_S6A_L3_ALT_HR_OST_NTC_F08_F08.json
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index fbfbe9ab90..e124bfddc2 100644
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index 7ad9673263..647a0a7304 100644
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index 7c75f4b5c2..95d81bd133 100644
--- a/datasets/K10_SST-NAVO-L4-GLOB-v01_1.0.json
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index 01c128f63f..6c269bbf7f 100644
--- a/datasets/L3S_LEO_AM-STAR-v2.80_2.80.json
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@@ -42,7 +42,7 @@
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index 470e2cfe47..059420963d 100644
--- a/datasets/L3S_LEO_DY-STAR-v2.81_2.81.json
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index 4d464cfb7e..3542a816fb 100644
--- a/datasets/L3S_LEO_PM-STAR-v2.81_2.81.json
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diff --git a/datasets/LEOLSTCMG30_001.json b/datasets/LEOLSTCMG30_001.json
index 2999563b58..74a1ba1c44 100644
--- a/datasets/LEOLSTCMG30_001.json
+++ b/datasets/LEOLSTCMG30_001.json
@@ -42,7 +42,7 @@
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index 1b4fe32473..236e61e1b0 100644
--- a/datasets/LEOLSTCMG30_002.json
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@@ -42,7 +42,7 @@
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index 02cb32af08..077895e1e6 100644
--- a/datasets/LGRIP30_001.json
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index a0047d4b08..b9769de1a7 100644
--- a/datasets/LOCSS_L1_V1_1.0.json
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diff --git a/datasets/LPJ_EOSIM_L2_DCH4E_001.json b/datasets/LPJ_EOSIM_L2_DCH4E_001.json
index 7f9566afaa..93d8391080 100644
--- a/datasets/LPJ_EOSIM_L2_DCH4E_001.json
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@@ -42,7 +42,7 @@
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index b1e9a5c278..c5787a82c4 100644
--- a/datasets/LPJ_EOSIM_L2_DCH4E_LL_001.json
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index 1d05854daa..f7c106f5ec 100644
--- a/datasets/LPJ_EOSIM_L2_MCH4E_001.json
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@@ -42,7 +42,7 @@
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index bf35982dfd..1a9a41c51c 100644
--- a/datasets/LPJ_EOSIM_L2_MCH4E_LL_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/LPJ_L2_SSREF_001.json b/datasets/LPJ_L2_SSREF_001.json
index 288cbfcc81..833464a080 100644
--- a/datasets/LPJ_L2_SSREF_001.json
+++ b/datasets/LPJ_L2_SSREF_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/LPJ_L2_SSREF_002.json b/datasets/LPJ_L2_SSREF_002.json
index 26901a2caa..1611164e23 100644
--- a/datasets/LPJ_L2_SSREF_002.json
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diff --git a/datasets/LandCoverNet South America_1.json b/datasets/LandCoverNet South America_1.json
index 3f28f08a0a..04f2f85de1 100644
--- a/datasets/LandCoverNet South America_1.json	
+++ b/datasets/LandCoverNet South America_1.json	
@@ -42,7 +42,7 @@
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diff --git a/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json b/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json
index 371f8b10a6..2c29227a4c 100644
--- a/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json
+++ b/datasets/LiDAR_Forest_Inventory_Brazil_1644_1.json
@@ -111,7 +111,7 @@
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index 07f2388a52..17c22f5593 100644
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index ad3a75db05..b074035510 100644
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diff --git a/datasets/MCD18A1_062.json b/datasets/MCD18A1_062.json
index b51a4995e8..f9d6353787 100644
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index 2b8b99aa7b..bf2543bf21 100644
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index 1e205a47ee..aef7a9e998 100644
--- a/datasets/MCD43D35_061.json
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index 33c5277490..d81f394f7d 100644
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index bd13732fa3..c484537a5b 100644
--- a/datasets/MCD43D37_061.json
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index b8da84e08e..0150e0160a 100644
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index 7a0c0d766d..7eeeeb3fa3 100644
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index 60e0da0b4d..565c4ceb78 100644
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index e67aca95a9..d759ce58ff 100644
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index d943987141..030599da98 100644
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index 6dc06159da..bc4ff578b1 100644
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index e518470717..4c13b1a98e 100644
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index f038227a62..d9b7e01d91 100644
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index 561d6448aa..c9c7d9adca 100644
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diff --git a/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json b/datasets/MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0_1.0.json
index da2eb0d21e..beeaf9dfe9 100644
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index 0f25d648a8..258147fe4a 100644
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index 994d408887..6746a393a4 100644
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index a8aed86587..7e75dbeea5 100644
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index 2d75a73097..6b65948daa 100644
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index 82e1940b76..0f154e05a7 100644
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index a4d2cd6046..27222340b3 100644
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index bbc5aa7de2..ecb30bbb03 100644
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index 266ac015c4..08f065d84d 100644
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index 7782e3807c..0c94443fee 100644
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index 58e53a4063..0ae5d2b2f5 100644
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index fb5cb35b3e..47ef2d30da 100644
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index 17385a954e..6798b3c260 100644
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index 231c7b19de..2a4e2f09d0 100644
--- a/datasets/MOD11B2_061.json
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index e81e26e4f9..d331840c68 100644
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diff --git a/datasets/MOD11C1_061.json b/datasets/MOD11C1_061.json
index 7481f22397..621d11ea11 100644
--- a/datasets/MOD11C1_061.json
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index d660e80d20..a7ab5f977e 100644
--- a/datasets/MOD11C2_061.json
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index 66c87a3826..1ca6eaa1ec 100644
--- a/datasets/MOD11C3_061.json
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index c31c6154d2..4b0b0e5223 100644
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index 2db8d39239..d689cdf33f 100644
--- a/datasets/MOD17A2HGF_061.json
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index bc5489c15c..3784788c18 100644
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index b87c799c1f..4d230b1754 100644
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index 5d68ebdd66..91f34ebdf7 100644
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index 5096215b20..93be372460 100644
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index 35b4e54b10..5b31d9d6de 100644
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index 48f54a1d95..d6a947ae13 100644
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index 962d233d8b..99d4d948c6 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json
+++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json
index da4dbbd668..3a1bcd4ba7 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json
+++ b/datasets/MODIS_AQUA_L3_SST_MID-IR_8DAY_9KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json
index 689290b1ab..b2b0fe6f41 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_ANNUAL_9KM_NIGHTTIME_V2019.0_2019.0.json
index 82f1b64334..010d8c1e70 100644
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index 842cc903aa..354adb392d 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json
index 59a8ae69a3..fc1b428b46 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_DAILY_9KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json
index 3d5ffa8a6e..e903a3d56c 100644
--- a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json
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diff --git a/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_MID-IR_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json
index ae01bc6681..0820e99c91 100644
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index 01ac4a1c29..1c02512c1f 100644
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index ec671ef293..61d9fd83ef 100644
--- a/datasets/MODIS_AQUA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json
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index 1b129363f1..641dd40b9a 100644
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index 3779040751..fb6990c5cc 100644
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index 8fbf14f4ff..d6e348cdf8 100644
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index cb049da590..ab5c609806 100644
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index dfb0d66e89..fae9e35a8f 100644
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index f12065dc31..110f450901 100644
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index 54eef1364f..fbcff57fbc 100644
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index a2a8cb20bf..50bdaaf2d9 100644
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index 833cb0c0e4..0bcde526bc 100644
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index 7ce84fed4c..5579d00726 100644
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index 74d43f4a06..4b4c8b87c8 100644
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diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0.json
index a5e301ad81..f4bcaaeaa4 100644
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index 501e82304b..4634a00e99 100644
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diff --git a/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0.json
index 4318f17ecd..792727b27d 100644
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index abf3ccf86e..e77a1ca83a 100644
--- a/datasets/MODIS_T-JPL-L2P-v2019.0_2019.0.json
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index a6810c5e94..6536685285 100644
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index 205b23c271..04fc1b26d0 100644
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diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_ANNUAL_4KM_NIGHTTIME_V2019.0_2019.0.json
index 2bfb6a13be..9d791609a5 100644
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index 088deb0408..3cdb4cee18 100644
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diff --git a/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_MID-IR_DAILY_4KM_NIGHTTIME_V2019.0_2019.0.json
index 61d629d892..e1d75e93eb 100644
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index 972e494cfe..b1f5cb6920 100644
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index 43e69971e1..b7c1051f50 100644
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index 32d8f86b27..51571c2c43 100644
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diff --git a/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json b/datasets/MODIS_TERRA_L3_SST_THERMAL_8DAY_4KM_NIGHTTIME_V2019.0_2019.0.json
index 0cb63e13ea..29cc11f57a 100644
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index 366677ca4f..b121aec446 100644
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index bd61da9c68..7cebfbf2b9 100644
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index 668d0dfc40..bfdc4e90e1 100644
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index 82bc240958..0257103b00 100644
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index 333e9242f4..9330333ce9 100644
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index a609678669..097e88f27c 100644
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index b9e7582c73..a7488dc8c2 100644
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index 4af7c6fccc..e28cfe5efa 100644
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index b39bfb4e71..39d460c31a 100644
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index 567ddeb502..c20c36351c 100644
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index fad08de24d..f9334210a6 100644
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index 93e9b92077..4b66419007 100644
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index 5e5103aaec..0f3fbbc379 100644
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index 8cf891b2c1..0760c6038b 100644
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index 53af1f3933..de348dfab8 100644
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index 7729d542b0..8e4cece3b4 100644
--- a/datasets/MSG02-OSPO-L2P-v1.0_1.0.json
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index 1b303d9118..7693829ed1 100644
--- a/datasets/MSG03-OSPO-L2P-v1.0_1.0.json
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index 06f8f03315..4ac4255300 100644
--- a/datasets/MSG04-OSPO-L2P-v1.0_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MSLSP30NA_011.json b/datasets/MSLSP30NA_011.json
index 34e3c0a127..23b86e3826 100644
--- a/datasets/MSLSP30NA_011.json
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diff --git a/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json b/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json
index ba628c90fd..5023e28ac8 100644
--- a/datasets/MTSAT2-OSPO-L2P-v1.0_1.0.json
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@@ -42,7 +42,7 @@
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index 62a5369e78..3eeea19590 100644
--- a/datasets/MUR-JPL-L4-GLOB-v4.1_4.1.json
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diff --git a/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json b/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json
index b06c50aac5..9b93b6b275 100644
--- a/datasets/MUR25-JPL-L4-GLOB-v04.2_4.2.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json
index a6896009af..a023a87360 100644
--- a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json
+++ b/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.0_5.0.json
@@ -42,7 +42,7 @@
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index db523312b2..550cda32f4 100644
--- a/datasets/MW_IR_OI-REMSS-L4-GLOB-v5.1_5.1.json
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diff --git a/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json b/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json
index eb2d23ef34..6a4e6ac517 100644
--- a/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json
+++ b/datasets/MW_OI-REMSS-L4-GLOB-v5.0_5.0.json
@@ -42,7 +42,7 @@
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index 8e4cf2e3ff..b827e68b44 100644
--- a/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json
+++ b/datasets/MW_OI-REMSS-L4-GLOB-v5.1_5.1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD09A1_061.json b/datasets/MYD09A1_061.json
index 6a60a2ac2c..2720dae703 100644
--- a/datasets/MYD09A1_061.json
+++ b/datasets/MYD09A1_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD09CMG_061.json b/datasets/MYD09CMG_061.json
index 5958decb5f..14425e3ba4 100644
--- a/datasets/MYD09CMG_061.json
+++ b/datasets/MYD09CMG_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD09GA_061.json b/datasets/MYD09GA_061.json
index afe6555e0c..010d19a42d 100644
--- a/datasets/MYD09GA_061.json
+++ b/datasets/MYD09GA_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD09GQ_061.json b/datasets/MYD09GQ_061.json
index 23c3837334..66338d1131 100644
--- a/datasets/MYD09GQ_061.json
+++ b/datasets/MYD09GQ_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD09Q1_061.json b/datasets/MYD09Q1_061.json
index bdee6a6383..2db512ed25 100644
--- a/datasets/MYD09Q1_061.json
+++ b/datasets/MYD09Q1_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD11A1_061.json b/datasets/MYD11A1_061.json
index 7717316981..94b0adca0c 100644
--- a/datasets/MYD11A1_061.json
+++ b/datasets/MYD11A1_061.json
@@ -42,7 +42,7 @@
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index f135318608..07b04a0469 100644
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@@ -42,7 +42,7 @@
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index 766973bf3f..b09515620c 100644
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@@ -42,7 +42,7 @@
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index 80f8435ecb..a79a6262e8 100644
--- a/datasets/MYD11B2_061.json
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@@ -42,7 +42,7 @@
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index ead04187f7..409c6ee91b 100644
--- a/datasets/MYD11B3_061.json
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@@ -42,7 +42,7 @@
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index ff0aa34bb0..75104dfe19 100644
--- a/datasets/MYD11C1_061.json
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diff --git a/datasets/MYD11C2_061.json b/datasets/MYD11C2_061.json
index 76d76b7962..79ae9a4bee 100644
--- a/datasets/MYD11C2_061.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD11C3_061.json b/datasets/MYD11C3_061.json
index 2c4d8b83fe..0cd5eb6e6f 100644
--- a/datasets/MYD11C3_061.json
+++ b/datasets/MYD11C3_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD11_L2_061.json b/datasets/MYD11_L2_061.json
index 058fe8e2c5..39f34b2857 100644
--- a/datasets/MYD11_L2_061.json
+++ b/datasets/MYD11_L2_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD13A1_061.json b/datasets/MYD13A1_061.json
index 2769bf9320..e2c2238e50 100644
--- a/datasets/MYD13A1_061.json
+++ b/datasets/MYD13A1_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD13A2_061.json b/datasets/MYD13A2_061.json
index 3474779af5..49c08cf32b 100644
--- a/datasets/MYD13A2_061.json
+++ b/datasets/MYD13A2_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD13A3_061.json b/datasets/MYD13A3_061.json
index 6844c777e0..875fb0f725 100644
--- a/datasets/MYD13A3_061.json
+++ b/datasets/MYD13A3_061.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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index 2399d5f9ca..517dfe9ba7 100644
--- a/datasets/MYD13C1_061.json
+++ b/datasets/MYD13C1_061.json
@@ -42,7 +42,7 @@
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index 75927858c8..c75ffc184a 100644
--- a/datasets/MYD13C2_061.json
+++ b/datasets/MYD13C2_061.json
@@ -42,7 +42,7 @@
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index e0ff816981..8dd56c2127 100644
--- a/datasets/MYD13Q1_061.json
+++ b/datasets/MYD13Q1_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD14A1_061.json b/datasets/MYD14A1_061.json
index bce000b668..5acf356cd2 100644
--- a/datasets/MYD14A1_061.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD14A2_061.json b/datasets/MYD14A2_061.json
index 8f9789951a..a32b248373 100644
--- a/datasets/MYD14A2_061.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD14_061.json b/datasets/MYD14_061.json
index da15ea9302..c0932847b7 100644
--- a/datasets/MYD14_061.json
+++ b/datasets/MYD14_061.json
@@ -42,7 +42,7 @@
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index 151db5ec5e..40a0d84dbc 100644
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD16A2GF_061.json b/datasets/MYD16A2GF_061.json
index 5bb133f9a7..c5b57dfdd2 100644
--- a/datasets/MYD16A2GF_061.json
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index a1bfdbb0f0..877680c596 100644
--- a/datasets/MYD16A2_061.json
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@@ -42,7 +42,7 @@
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index f2aca70141..45653309a8 100644
--- a/datasets/MYD16A3GF_061.json
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@@ -42,7 +42,7 @@
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index 5c2ee973c5..6095f6b89d 100644
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index d728374b8b..15215a3d38 100644
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index 4ccd4c0bb0..718509cc37 100644
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index 228fc07d92..3526d68c5b 100644
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index 8aa488d990..5d125a2c0e 100644
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index 506c0b91dd..53b6e0fc58 100644
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diff --git a/datasets/MYD21C1_061.json b/datasets/MYD21C1_061.json
index 8339729a17..70a525c160 100644
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@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD21C2_061.json b/datasets/MYD21C2_061.json
index e60adfe07b..1a7ac6c9e4 100644
--- a/datasets/MYD21C2_061.json
+++ b/datasets/MYD21C2_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD21C3_061.json b/datasets/MYD21C3_061.json
index 634bb8d610..6568b31dea 100644
--- a/datasets/MYD21C3_061.json
+++ b/datasets/MYD21C3_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD21_061.json b/datasets/MYD21_061.json
index 0a5327d273..a07d899c03 100644
--- a/datasets/MYD21_061.json
+++ b/datasets/MYD21_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD28C2_061.json b/datasets/MYD28C2_061.json
index b345f56208..db457bf3c2 100644
--- a/datasets/MYD28C2_061.json
+++ b/datasets/MYD28C2_061.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/MYD28C3_061.json b/datasets/MYD28C3_061.json
index 0d44c5c54a..2a6d7949d6 100644
--- a/datasets/MYD28C3_061.json
+++ b/datasets/MYD28C3_061.json
@@ -42,7 +42,7 @@
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index 08729cbc79..1f376b363b 100644
--- a/datasets/Marine Debris Archive (MARIDA)_1.json	
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index dee66f6d48..70623fae3f 100644
--- a/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json	
+++ b/datasets/Marine Debris Dataset for Object Detection in Planetscope Imagery_1.json	
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index f7e9fe538c..3e9f078d37 100644
--- a/datasets/N21-VIIRS-L2P-ACSPO-v2.80_2.80.json
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index 0c07b8e52e..5f76ff229f 100644
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diff --git a/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json b/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json
index c003d2aef5..56cdb406da 100644
--- a/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json
+++ b/datasets/NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1.json
@@ -76,10 +76,10 @@
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diff --git a/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json b/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json
index fb589e7728..a04b90e8a4 100644
--- a/datasets/NAAMES_Aerosol_AircraftInSitu_Data_1.json
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@@ -42,7 +42,7 @@
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index 41688b77a9..81ea908897 100644
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index 7bc426aceb..3fb217e719 100644
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index 6c5fa86d11..41e82a1d5f 100644
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index 5cd4697cb0..b0dfe19349 100644
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index 4e18d6cd89..02125614e3 100644
--- a/datasets/NSCAT_BYU_L3_OW_SIGMA0_ENHANCED_1.json
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diff --git a/datasets/NSCAT_LEVEL_1.7_V2_2.json b/datasets/NSCAT_LEVEL_1.7_V2_2.json
index 6931b18728..b249770a26 100644
--- a/datasets/NSCAT_LEVEL_1.7_V2_2.json
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diff --git a/datasets/NSCAT_LEVEL_2_V2_2.json b/datasets/NSCAT_LEVEL_2_V2_2.json
index 7c224f0612..e5898b6b21 100644
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diff --git a/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json b/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json
index c20219757c..1e2e487d03 100644
--- a/datasets/NSCAT_LEVEL_3_BROWSE_IMAGES_2.json
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index 7ab269404a..eaffd24bac 100644
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index b2a02df108..2275743b8f 100644
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diff --git a/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3_RL06.1Mv03.json b/datasets/OCEAN_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.1_V3_RL06.1Mv03.json
index b8276e0c6b..f62b28239e 100644
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index 708624a9ee..6a257da7be 100644
--- a/datasets/OISSS_L4_multimission_7day_v1_1.0.json
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@@ -42,7 +42,7 @@
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index 0401212d06..cecb7c0e57 100644
--- a/datasets/OISSS_L4_multimission_7day_v2_2.0.json
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index e4a7fb7199..11c53308ef 100644
--- a/datasets/OISSS_L4_multimission_monthly_v1_1.0.json
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index 25294b3f0f..ef37ef2ec7 100644
--- a/datasets/OISSS_L4_multimission_monthly_v2_2.0.json
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index ca03a9ef9b..7ea499c1a6 100644
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index d510796fd9..3cd053a048 100644
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index 11479053e1..87e2ce516f 100644
--- a/datasets/OMG_L1B_AIRGRAV_1.json
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index abd7405cf2..26731d20b7 100644
--- a/datasets/OMG_L1_FLOAT_ALAMO_1.json
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index 6d8b555470..a5fd6bacbc 100644
--- a/datasets/OMG_L1_FLOAT_APEX_1.json
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diff --git a/datasets/OMG_L2_AXBT_1.json b/datasets/OMG_L2_AXBT_1.json
index db66033f1e..96b7945c33 100644
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index ef178cff72..acb99df02b 100644
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index d8fb786e61..0b1188babb 100644
--- a/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json
+++ b/datasets/OMG_L3_ICE_ELEV_GLISTINA_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json b/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json
index b2f62d94b7..734bcd36e2 100644
--- a/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json
+++ b/datasets/OMG_NARWHALS_MOORING_TEMP_CTD_1.0_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json b/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json
index 49ece84c26..12d07b22af 100644
--- a/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json
+++ b/datasets/OMG_NARWHALS_SHIPBOARD_CTD_1.0_1.0.json
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diff --git a/datasets/OPERA_L2_RTC-S1_V1_1.json b/datasets/OPERA_L2_RTC-S1_V1_1.json
index 82b40dd221..7b9722ba73 100644
--- a/datasets/OPERA_L2_RTC-S1_V1_1.json
+++ b/datasets/OPERA_L2_RTC-S1_V1_1.json
@@ -86,7 +86,7 @@
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                 [
-                    "2023-10-04T00:00:00Z",
+                    "2021-01-01T00:00:00Z",
                     null
                 ]
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diff --git a/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json b/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json
index 2bc623e70e..7f66d40633 100644
--- a/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json
+++ b/datasets/OPERA_L3_DIST-ALERT-HLS_V1_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json b/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json
index 0e391ef872..cdc29516ba 100644
--- a/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json
+++ b/datasets/OPERA_L3_DIST-ANN-HLS_V1_1.json
@@ -42,7 +42,7 @@
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index 9c424ebc15..2325f971b8 100644
--- a/datasets/OPERA_L3_DSWX-HLS_V1_1.0.json
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index 43f1899674..7ce6dc08e8 100644
--- a/datasets/OPERA_L3_DSWX-S1_V1_1.0.json
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index c7efc0f814..6ec8d4248f 100644
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index ceee5cda88..c91c7ab20c 100644
--- a/datasets/OSCAR_L4_OC_1deg_1.json
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@@ -42,7 +42,7 @@
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index 3c619894b4..b3f397c43e 100644
--- a/datasets/OSCAR_L4_OC_FINAL_V2.0_2.0.json
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@@ -42,7 +42,7 @@
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index 4f9d2dcc49..692e40091f 100644
--- a/datasets/OSCAR_L4_OC_INTERIM_V2.0_2.0.json
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index eff250b43e..53506a6fc5 100644
--- a/datasets/OSCAR_L4_OC_NRT_V2.0_2.0.json
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diff --git a/datasets/OSCAR_L4_OC_third-deg_1.json b/datasets/OSCAR_L4_OC_third-deg_1.json
index 5b38d04f57..d4267f4b6f 100644
--- a/datasets/OSCAR_L4_OC_third-deg_1.json
+++ b/datasets/OSCAR_L4_OC_third-deg_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/OSCAR_L4_OC_third-deg_YEARLY_1.json b/datasets/OSCAR_L4_OC_third-deg_YEARLY_1.json
index 0233a12478..100f7bbe67 100644
--- a/datasets/OSCAR_L4_OC_third-deg_YEARLY_1.json
+++ b/datasets/OSCAR_L4_OC_third-deg_YEARLY_1.json
@@ -42,7 +42,7 @@
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index ab9aaee18b..72a147bdec 100644
--- a/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json
+++ b/datasets/OSTIA-UKMO-L4-GLOB-REP-v2.0_2.0.json
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index d0af00a847..69c61460d4 100644
--- a/datasets/OSTIA-UKMO-L4-GLOB-v2.0_2.0.json
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diff --git a/datasets/Open Cities AI Challenge Dataset_1.json b/datasets/Open Cities AI Challenge Dataset_1.json
index c656f23326..c214b41480 100644
--- a/datasets/Open Cities AI Challenge Dataset_1.json	
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diff --git a/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json b/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json
index 200eaa1247..e0a0beb70b 100644
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+++ b/datasets/PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2_2.json
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diff --git a/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json b/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json
index e10e9e9cee..37accf8e8e 100644
--- a/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json
+++ b/datasets/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2_2.json
@@ -42,7 +42,7 @@
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index fa9fb09897..e5b12014c0 100644
--- a/datasets/PRESWOT_HYDRO_L2_GREALM_LAKE_HEIGHT_V2_2.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json b/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json
index 346397b0ba..23c453cd55 100644
--- a/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json
+++ b/datasets/PRESWOT_HYDRO_L3_LAKE_RESEVOIR_AREA_V2_2.json
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diff --git a/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json b/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json
index 4578302ce5..dad56e836a 100644
--- a/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json
+++ b/datasets/PRESWOT_HYDRO_L4_LAKE_STORAGE_TIME_SERIES_V2_2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/PRIM_SMAP_L2_V1_1.0.json b/datasets/PRIM_SMAP_L2_V1_1.0.json
index 7f6ea55d35..187f37510b 100644
--- a/datasets/PRIM_SMAP_L2_V1_1.0.json
+++ b/datasets/PRIM_SMAP_L2_V1_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/PlantVillage Crop Type Kenya_1.json b/datasets/PlantVillage Crop Type Kenya_1.json
index 42cf5fd62d..c8308875be 100644
--- a/datasets/PlantVillage Crop Type Kenya_1.json	
+++ b/datasets/PlantVillage Crop Type Kenya_1.json	
@@ -42,7 +42,7 @@
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index 7a39efdedd..f2c6a0f4cb 100644
--- a/datasets/QSCAT_ESDR_MODELED_L2_AUX_V1.0_1.0.json
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diff --git a/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json b/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json
index fc6858abf5..1f555cfd9c 100644
--- a/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json
+++ b/datasets/QSCAT_L1C_NONSPINNING_SIGMA0_WINDS_V2_2.0.json
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diff --git a/datasets/QSCAT_LEVEL_1B_V2_2.json b/datasets/QSCAT_LEVEL_1B_V2_2.json
index 6526ea8867..96e1bbd901 100644
--- a/datasets/QSCAT_LEVEL_1B_V2_2.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json
index 1dda15ec6f..82349e078b 100644
--- a/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json
+++ b/datasets/QSCAT_LEVEL_2B_OWV_COMP_12_3.json
@@ -42,7 +42,7 @@
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index 8027d26e8a..425899e0c3 100644
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index 23219df112..167d20f45b 100644
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index 068ca7a394..64e971aa0b 100644
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diff --git a/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json b/datasets/RAMSSA_09km-ABOM-L4-AUS-v01_1.0.json
index 3bb4fe1947..4ac7df13e7 100644
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diff --git a/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json b/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json
index 55eb018a22..920f3dbf22 100644
--- a/datasets/RECON_SEA_LEVEL_OST_L4_V1_1.json
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index 1d00919d43..3dd974a7cb 100644
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index 4ceaae27da..997ee34e37 100644
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index 16b3be0f19..4e58b40c77 100644
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index c4f6c4ec4e..69a33b9749 100644
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diff --git a/datasets/RSCAT_L1B_V2.0_2.0.json b/datasets/RSCAT_L1B_V2.0_2.0.json
index 0b0935c609..963cf1addb 100644
--- a/datasets/RSCAT_L1B_V2.0_2.0.json
+++ b/datasets/RSCAT_L1B_V2.0_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/RSCAT_L2A_12KM_V2.0_2.0.json b/datasets/RSCAT_L2A_12KM_V2.0_2.0.json
index ba656bed08..3809a736c8 100644
--- a/datasets/RSCAT_L2A_12KM_V2.0_2.0.json
+++ b/datasets/RSCAT_L2A_12KM_V2.0_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/RSCAT_L2A_25KM_V2.0_2.0.json b/datasets/RSCAT_L2A_25KM_V2.0_2.0.json
index 5edd2e69b3..13d0621922 100644
--- a/datasets/RSCAT_L2A_25KM_V2.0_2.0.json
+++ b/datasets/RSCAT_L2A_25KM_V2.0_2.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json
index 1f6809d028..4173e9efd1 100644
--- a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json
+++ b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V1_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json
index 1d1e6a510c..7617fca22f 100644
--- a/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json
+++ b/datasets/RSCAT_LEVEL_2B_OWV_CLIM_12_V2_2.0.json
@@ -42,7 +42,7 @@
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index c1ac6cdf96..ead59b6e09 100644
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index 48e4cfc17a..3f334aee89 100644
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index 29390a0358..afd9fbc17c 100644
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index 1f863576a7..42e16a0d8b 100644
--- a/datasets/RSS_WindSat_L1C_TB_V08.0_8.0.json
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index b2f40e237d..6d4bc581bc 100644
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index fe93686f9e..557249577f 100644
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index 321e143198..089570bba9 100644
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index 5894bd7a7c..49f5375cf1 100644
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index 6a05b5f88a..0b8d84a6b0 100644
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index 50ba8ff20d..b03527e6aa 100644
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index 70f9469166..a1d695f862 100644
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index a9b78870fb..17c648bbcb 100644
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index 0e981de57e..cc166eb772 100644
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index ca96772adf..187d747d79 100644
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index 82cafe1f7a..5f3ddd507d 100644
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index cfe43d934c..853d9cb1a9 100644
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index 7b57ffa7be..d29faa5b79 100644
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index 5558de7e19..ef5aea701e 100644
--- a/datasets/SASSIE_L2_SHIPBOARD_SALINITY_SNAKE_V1_1.json
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index f5b04b8aa8..2ec17e9f9d 100644
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index 1d3cbd62e1..6bc1a57acc 100644
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index bc14ed4e4b..2d38c38073 100644
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index 01d08436af..4cfd001b0e 100644
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index fec484ce83..75c0aa2867 100644
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index da783d8b54..5beaa8157d 100644
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index 87f1d0185c..b3831e4c64 100644
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index b680cab563..c1c9534b32 100644
--- a/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json
+++ b/datasets/SEVIRI_SST-OSISAF-L3C-v1.0_1.json
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index 77e1135108..1c9913999b 100644
--- a/datasets/SEVIRI_SST_DR-OSISAF-L3C-v1.0_1.0.json
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index 2fda645ccf..7501b32f01 100644
--- a/datasets/SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0.json
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index fa691e8a7c..7852d9d1ea 100644
--- a/datasets/SMAP_JPL_L2B_NRT_SSS_CAP_V5_5.0.json
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index 722ef3652e..823e769843 100644
--- a/datasets/SMAP_JPL_L2B_SSS_CAP_V5_5.0.json
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index 448952bda8..a6326f8e3f 100644
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index ff661f32e7..81fee9dd5a 100644
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index e35f65de0e..9c21635fb2 100644
--- a/datasets/SMAP_L1_L3_ANC_GEOS_1.json
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@@ -42,7 +42,7 @@
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index 1d88a216f3..050ecc2519 100644
--- a/datasets/SMAP_L1_L3_ANC_NOAA_1.json
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@@ -42,7 +42,7 @@
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index 78216ea5b0..f46d40e510 100644
--- a/datasets/SMAP_L1_L3_ANC_SATELLITE_1.json
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@@ -42,7 +42,7 @@
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index cc9fb34191..3b7c46b61a 100644
--- a/datasets/SMAP_L1_L3_ANC_STATIC_1.json
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@@ -42,7 +42,7 @@
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index 7e642ce498..f9f7bea207 100644
--- a/datasets/SMAP_L4_C_ANC_BPLUT_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json b/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json
index 89051932b0..222748da98 100644
--- a/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json
+++ b/datasets/SMAP_L4_C_ANC_FPAR_CLIM_1.json
@@ -42,7 +42,7 @@
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index b5b4a9b94a..ed61b9fed4 100644
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index 74bd457720..fc663f78fe 100644
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index 5b2a1777e3..6c1d10cc9a 100644
--- a/datasets/SMAP_L4_C_ANC_MET_1.json
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index e382f6ec27..83dbba92bd 100644
--- a/datasets/SMAP_L4_C_ANC_MET_LOG_1.json
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diff --git a/datasets/SMAP_L4_C_ANC_MET_RIP_1.json b/datasets/SMAP_L4_C_ANC_MET_RIP_1.json
index 5e99b1ae59..7197ca995f 100644
--- a/datasets/SMAP_L4_C_ANC_MET_RIP_1.json
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@@ -42,7 +42,7 @@
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index 39170f9c5c..6c1a6d3f81 100644
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index 9e3fd7f6cf..b63949d3fb 100644
--- a/datasets/SMAP_L4_C_ANC_MOD_RIP_1.json
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index f124813601..4ea5c8c7fb 100644
--- a/datasets/SMAP_L4_C_ANC_PARAM_1.json
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index 25eb35fc9e..72d7e3d7d5 100644
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index ef3d4c41b9..db55aea93d 100644
--- a/datasets/SMAP_L4_SM_ANC_CAT_TILE_1.json
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index 82bce3d910..1f64745271 100644
--- a/datasets/SMAP_L4_SM_ANC_CLIM_1.json
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diff --git a/datasets/SMAP_L4_SM_ANC_LOG_1.json b/datasets/SMAP_L4_SM_ANC_LOG_1.json
index 9bb997550c..97fc6105e7 100644
--- a/datasets/SMAP_L4_SM_ANC_LOG_1.json
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diff --git a/datasets/SMAP_L4_SM_ANC_PARAM_1.json b/datasets/SMAP_L4_SM_ANC_PARAM_1.json
index 495c62c725..84028db345 100644
--- a/datasets/SMAP_L4_SM_ANC_PARAM_1.json
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diff --git a/datasets/SMAP_L4_SM_ANC_RIP_1.json b/datasets/SMAP_L4_SM_ANC_RIP_1.json
index 99c63ce86c..971fb50740 100644
--- a/datasets/SMAP_L4_SM_ANC_RIP_1.json
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@@ -42,7 +42,7 @@
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index bb5ffdfe6d..beead7d2f0 100644
--- a/datasets/SMAP_L4_SM_ANC_RST_1.json
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index cdc1dda5e0..547137ebf3 100644
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index 8a5dc58843..233dcdd4cf 100644
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index c1b1b54c28..e4dcc3dc58 100644
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index fcbcdb9615..ae8e404663 100644
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index 94e337d9c1..3a6f321d6a 100644
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diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V4_4.0.json
index b8242f24a0..3cd2c33e7b 100644
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diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json
index f3321e1ac6..b6321647f8 100644
--- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5.3_5.3.json
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index df14ddea74..f4b6b910ff 100644
--- a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V5_5.0.json
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diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_8DAY-RUNNINGMEAN_V6_6.0.json
index 1a6e3d78bb..c71b3ff52d 100644
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diff --git a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json b/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json
index 3026ab1019..11f8ab7db9 100644
--- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V4_4.0.json
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index d60cb93ed7..a6ae1078a4 100644
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index 4f78d1c12a..b8cc505292 100644
--- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0.json
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index 51052758e8..5829dfac7a 100644
--- a/datasets/SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0.json
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index 2e01d6ba50..005c4bcbd6 100644
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index 726a991e86..d114f7c5d1 100644
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index f7c12f6038..106ddcc317 100644
--- a/datasets/SMODE_L1_DOPPLERSCATT_V1_1.json
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index 9c538629c1..0ce403e672 100644
--- a/datasets/SMODE_L1_MASS_DOPPVIS_V1_1.json
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diff --git a/datasets/SMODE_L1_MASS_DOPVISIBLE_V1_1.json b/datasets/SMODE_L1_MASS_DOPVISIBLE_V1_1.json
index 87de25d7ca..1e2b5ff29f 100644
--- a/datasets/SMODE_L1_MASS_DOPVISIBLE_V1_1.json
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index 80ea4ab7e8..e9a128cfe0 100644
--- a/datasets/SMODE_L1_MASS_HYPERSPECTRAL_V1_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SMODE_L1_MASS_LIDAR_V1_1.json b/datasets/SMODE_L1_MASS_LIDAR_V1_1.json
index d63acbcc1e..29d360514e 100644
--- a/datasets/SMODE_L1_MASS_LIDAR_V1_1.json
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index 573ea9df29..c8bed48a67 100644
--- a/datasets/SMODE_L1_MASS_LWIR_V1_1.json
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index ee9f0cee0a..c4c25bdba4 100644
--- a/datasets/SMODE_L1_MASS_VISIBLE_V1_1.json
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index d8732b1edd..09e9831b95 100644
--- a/datasets/SMODE_L1_PRISM_V1_1.json
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index 0eff3c60b6..5dc06f0ec9 100644
--- a/datasets/SMODE_L1_SAILDRONES_V1_1.json
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index 665d751b04..5e228a3c8f 100644
--- a/datasets/SMODE_L2_APEX_FLOAT_V1_1.json
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index 4d1d394c06..451bb80546 100644
--- a/datasets/SMODE_L2_DOPPLERSCATT_WINDS_CURRENT_V1_1.json
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index 624f0b920a..cdf349f5b3 100644
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index 8788635f53..2e803dcaf0 100644
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index db6a365207..a71fa8f370 100644
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index 9073d221e5..d98b6b535d 100644
--- a/datasets/SMODE_L2_MOSES_LWIR_SST_V1_1.json
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index cc1af3f342..a43da5748b 100644
--- a/datasets/SMODE_L2_PRISM_CHLA_V1_1.json
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index 1bac42bbb6..17843e6aff 100644
--- a/datasets/SMODE_L2_SAILDRONES_V1_1.json
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@@ -42,7 +42,7 @@
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index 42dc497a25..2ab2d97d35 100644
--- a/datasets/SMODE_L2_SEAGLIDERS_V1_1.json
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diff --git a/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json b/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json
index 7dd725fbdf..41a05dfb2e 100644
--- a/datasets/SMODE_L2_SHIPBOARD_ADCP_V1_1.json
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index 0cc2d085d9..5f6f802670 100644
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index 4af7ed5842..70fdc19111 100644
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index 86ca08aa0a..46b1a50ae6 100644
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index 83a989b5bd..eddcba0995 100644
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index 3cf53b19e3..f43e6f9903 100644
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index 497af92ff4..b2c156cdda 100644
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index e9faed6d72..00824e3bf9 100644
--- a/datasets/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1.json
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index 3879b766fe..5960c65942 100644
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index eab95634f0..22f5b48dc2 100644
--- a/datasets/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1.json
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index fabe5c726f..55409182aa 100644
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diff --git a/datasets/SMODE_L4_NCOM_V1_1.json b/datasets/SMODE_L4_NCOM_V1_1.json
index 741427d36f..d200b3cdba 100644
--- a/datasets/SMODE_L4_NCOM_V1_1.json
+++ b/datasets/SMODE_L4_NCOM_V1_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNDRAQIL2CCPCCR_2.json b/datasets/SNDRAQIL2CCPCCR_2.json
index 0568bb01e1..9ec5907ff9 100644
--- a/datasets/SNDRAQIL2CCPCCR_2.json
+++ b/datasets/SNDRAQIL2CCPCCR_2.json
@@ -125,6 +125,14 @@
             "roles": [
                 "data"
             ]
+        },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL2CCPCCR_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.",
+            "roles": [
+                "metadata"
+            ]
         }
     }
 }
\ No newline at end of file
diff --git a/datasets/SNDRAQIL2CCPRET_2.json b/datasets/SNDRAQIL2CCPRET_2.json
index 44bc5cdccb..a824dcae32 100644
--- a/datasets/SNDRAQIL2CCPRET_2.json
+++ b/datasets/SNDRAQIL2CCPRET_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL2CCPRET_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level2_SNDRAQIL2CCPRET_2_": {
             "href": "s3://gesdisc-cumulus-prod-protected/Aqua_Sounder_Level2/SNDRAQIL2CCPRET.2/",
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diff --git a/datasets/SNDRAQIL3CDCCP_2.json b/datasets/SNDRAQIL3CDCCP_2.json
index 789c6143c1..2897e59673 100644
--- a/datasets/SNDRAQIL3CDCCP_2.json
+++ b/datasets/SNDRAQIL3CDCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL3CDCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "https://disc.gsfc.nasa.gov/datacollection/  ",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3CDCCP_2_": {
             "href": "s3://gesdisc-cumulus-prod-protected/Aqua_Sounder_Level3/SNDRAQIL3CDCCP.2/",
             "title": "gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3CDCCP_2_",
diff --git a/datasets/SNDRAQIL3CMCCP_2.json b/datasets/SNDRAQIL3CMCCP_2.json
index 6f10ff7d6f..55d775e1de 100644
--- a/datasets/SNDRAQIL3CMCCP_2.json
+++ b/datasets/SNDRAQIL3CMCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL3CMCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.\n",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3CMCCP_2_": {
             "href": "s3://gesdisc-cumulus-prod-protected/Aqua_Sounder_Level3/SNDRAQIL3CMCCP.2/",
             "title": "gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3CMCCP_2_",
diff --git a/datasets/SNDRAQIL3SDCCP_2.json b/datasets/SNDRAQIL3SDCCP_2.json
index 6e366222ac..c1acba72a1 100644
--- a/datasets/SNDRAQIL3SDCCP_2.json
+++ b/datasets/SNDRAQIL3SDCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL3SDCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.\n",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3SDCCP_2_": {
             "href": "s3://gesdisc-cumulus-prod-protected/Aqua_Sounder_Level3/SNDRAQIL3SDCCP.2/",
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diff --git a/datasets/SNDRAQIL3SMCCP_2.json b/datasets/SNDRAQIL3SMCCP_2.json
index 1aa2015fe4..f68a4928e1 100644
--- a/datasets/SNDRAQIL3SMCCP_2.json
+++ b/datasets/SNDRAQIL3SMCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIL3SMCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Dataset Landing Page",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIL3SMCCP_2_": {
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diff --git a/datasets/SNDRAQIML2CCPCCR_2.json b/datasets/SNDRAQIML2CCPCCR_2.json
index 68472c446a..4c238687c5 100644
--- a/datasets/SNDRAQIML2CCPCCR_2.json
+++ b/datasets/SNDRAQIML2CCPCCR_2.json
@@ -125,6 +125,14 @@
             "roles": [
                 "data"
             ]
+        },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML2CCPCCR_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.",
+            "roles": [
+                "metadata"
+            ]
         }
     }
 }
\ No newline at end of file
diff --git a/datasets/SNDRAQIML2CCPRET_2.json b/datasets/SNDRAQIML2CCPRET_2.json
index 9cda5c05c8..068e1ea0fe 100644
--- a/datasets/SNDRAQIML2CCPRET_2.json
+++ b/datasets/SNDRAQIML2CCPRET_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML2CCPRET_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.\n",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level2_SNDRAQIML2CCPRET_2_": {
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diff --git a/datasets/SNDRAQIML3CDCCP_2.json b/datasets/SNDRAQIML3CDCCP_2.json
index 0b839b260e..71608304aa 100644
--- a/datasets/SNDRAQIML3CDCCP_2.json
+++ b/datasets/SNDRAQIML3CDCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML3CDCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.\n",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIML3CDCCP_2_": {
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diff --git a/datasets/SNDRAQIML3CMCCP_2.json b/datasets/SNDRAQIML3CMCCP_2.json
index 1eb2649d4f..d73af35f21 100644
--- a/datasets/SNDRAQIML3CMCCP_2.json
+++ b/datasets/SNDRAQIML3CMCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML3CMCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIML3CMCCP_2_": {
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diff --git a/datasets/SNDRAQIML3SDCCP_2.json b/datasets/SNDRAQIML3SDCCP_2.json
index 43b1d91c2c..f2d81a80c1 100644
--- a/datasets/SNDRAQIML3SDCCP_2.json
+++ b/datasets/SNDRAQIML3SDCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML3SDCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.\n",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIML3SDCCP_2_": {
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diff --git a/datasets/SNDRAQIML3SMCCP_2.json b/datasets/SNDRAQIML3SMCCP_2.json
index 11c08bc9ac..8355aeddc3 100644
--- a/datasets/SNDRAQIML3SMCCP_2.json
+++ b/datasets/SNDRAQIML3SMCCP_2.json
@@ -126,6 +126,14 @@
                 "data"
             ]
         },
+        "provider_metadata": {
+            "href": "https://disc.gsfc.nasa.gov/datacollection/SNDRAQIML3SMCCP_2.html",
+            "title": "Provider Metadata",
+            "description": "Access the dataset landing page from the GES DISC website.",
+            "roles": [
+                "metadata"
+            ]
+        },
         "s3_gesdisc_cumulus_prod_protected_Aqua_Sounder_Level3_SNDRAQIML3SMCCP_2_": {
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diff --git a/datasets/SNEX17_KT15_TB_1.json b/datasets/SNEX17_KT15_TB_1.json
index 76e99bfde0..06551f3e27 100644
--- a/datasets/SNEX17_KT15_TB_1.json
+++ b/datasets/SNEX17_KT15_TB_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_P3V_1.json b/datasets/SNEX17_P3V_1.json
index 99dfcf84c4..88d471aafd 100644
--- a/datasets/SNEX17_P3V_1.json
+++ b/datasets/SNEX17_P3V_1.json
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diff --git a/datasets/SNEX17_QWIP_ST_1.json b/datasets/SNEX17_QWIP_ST_1.json
index 1d6eb7dd8e..6d9955c6a8 100644
--- a/datasets/SNEX17_QWIP_ST_1.json
+++ b/datasets/SNEX17_QWIP_ST_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SBR_1.json b/datasets/SNEX17_SBR_1.json
index fb0c7c37ea..8acf55a262 100644
--- a/datasets/SNEX17_SBR_1.json
+++ b/datasets/SNEX17_SBR_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SMP2_1.json b/datasets/SNEX17_SMP2_1.json
index e4dd5d2432..8c871cdeb4 100644
--- a/datasets/SNEX17_SMP2_1.json
+++ b/datasets/SNEX17_SMP2_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SMP_1.json b/datasets/SNEX17_SMP_1.json
index be209cdd58..7fa923e6d9 100644
--- a/datasets/SNEX17_SMP_1.json
+++ b/datasets/SNEX17_SMP_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SSA_1.json b/datasets/SNEX17_SSA_1.json
index 461b0014b0..998148991c 100644
--- a/datasets/SNEX17_SSA_1.json
+++ b/datasets/SNEX17_SSA_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SSD_1.json b/datasets/SNEX17_SSD_1.json
index 8838ceef0d..391bfdc96b 100644
--- a/datasets/SNEX17_SSD_1.json
+++ b/datasets/SNEX17_SSD_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_SnowSAR_1.json b/datasets/SNEX17_SnowSAR_1.json
index cea400957b..44489c59fe 100644
--- a/datasets/SNEX17_SnowSAR_1.json
+++ b/datasets/SNEX17_SnowSAR_1.json
@@ -42,7 +42,7 @@
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@@ -53,7 +53,7 @@
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index c0caa9f273..befe73a471 100644
--- a/datasets/SNEX17_SnowSAR_Raw_1.json
+++ b/datasets/SNEX17_SnowSAR_Raw_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX17_TLI_1.json b/datasets/SNEX17_TLI_1.json
index 33b978ad3e..6732e64f45 100644
--- a/datasets/SNEX17_TLI_1.json
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diff --git a/datasets/SNEX17_UWScat_1.json b/datasets/SNEX17_UWScat_1.json
index 572a5206e2..6f025000de 100644
--- a/datasets/SNEX17_UWScat_1.json
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diff --git a/datasets/SNEX20_A19_GSM_1.json b/datasets/SNEX20_A19_GSM_1.json
index faf836347c..c8e92949b7 100644
--- a/datasets/SNEX20_A19_GSM_1.json
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@@ -42,7 +42,7 @@
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index dfefb00752..a5bf64e0aa 100644
--- a/datasets/SNEX20_A19_SD_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_A19_SP_1.json b/datasets/SNEX20_A19_SP_1.json
index f742a205ae..2c92a35a7a 100644
--- a/datasets/SNEX20_A19_SP_1.json
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index 5a034598b0..8a7f1be894 100644
--- a/datasets/SNEX20_A19_SWE_1.json
+++ b/datasets/SNEX20_A19_SWE_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_BR_BSU_SMP_1.json b/datasets/SNEX20_BR_BSU_SMP_1.json
index a1585894d0..03bb2bec38 100644
--- a/datasets/SNEX20_BR_BSU_SMP_1.json
+++ b/datasets/SNEX20_BR_BSU_SMP_1.json
@@ -42,7 +42,7 @@
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index e3c22c357e..ce29d3ccfe 100644
--- a/datasets/SNEX20_BSU_CMP_Raw_1.json
+++ b/datasets/SNEX20_BSU_CMP_Raw_1.json
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index ac7b37548b..b10e159de2 100644
--- a/datasets/SNEX20_BSU_CMP_SWE_1.json
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diff --git a/datasets/SNEX20_BSU_CMP_TT_1.json b/datasets/SNEX20_BSU_CMP_TT_1.json
index a13691ff73..e5245f6ab6 100644
--- a/datasets/SNEX20_BSU_CMP_TT_1.json
+++ b/datasets/SNEX20_BSU_CMP_TT_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_BSU_GPR_1.json b/datasets/SNEX20_BSU_GPR_1.json
index 3f0d1256ac..a98aa0d257 100644
--- a/datasets/SNEX20_BSU_GPR_1.json
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@@ -42,7 +42,7 @@
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index 11425c52ac..bc997b1aa7 100644
--- a/datasets/SNEX20_BSU_GPR_Raw_1.json
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@@ -42,7 +42,7 @@
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index 5ec9ed83ac..10affd551e 100644
--- a/datasets/SNEX20_COCP_GPR_1.json
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@@ -42,7 +42,7 @@
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index 25fc3992f9..4563598cf2 100644
--- a/datasets/SNEX20_COCP_GPR_Raw_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_COCP_SPD_1.json b/datasets/SNEX20_COCP_SPD_1.json
index defcd7a202..07fb34cc95 100644
--- a/datasets/SNEX20_COCP_SPD_1.json
+++ b/datasets/SNEX20_COCP_SPD_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_CRSM_1.json b/datasets/SNEX20_CRSM_1.json
index bde7575400..1fe36c6b0e 100644
--- a/datasets/SNEX20_CRSM_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_CSSM_1.json b/datasets/SNEX20_CSSM_1.json
index b58ed66cef..274f2b68e7 100644
--- a/datasets/SNEX20_CSSM_1.json
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diff --git a/datasets/SNEX20_DSM_1.json b/datasets/SNEX20_DSM_1.json
index 00010a9e6f..599f1dada1 100644
--- a/datasets/SNEX20_DSM_1.json
+++ b/datasets/SNEX20_DSM_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_GIS_REF_1.json b/datasets/SNEX20_GIS_REF_1.json
index fa077a9ec1..2b5e9afda8 100644
--- a/datasets/SNEX20_GIS_REF_1.json
+++ b/datasets/SNEX20_GIS_REF_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_GM_CSU_GPR_1.json b/datasets/SNEX20_GM_CSU_GPR_1.json
index cd42b34506..af41c6dbca 100644
--- a/datasets/SNEX20_GM_CSU_GPR_1.json
+++ b/datasets/SNEX20_GM_CSU_GPR_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_GM_CSU_GPR_RAW_1.json b/datasets/SNEX20_GM_CSU_GPR_RAW_1.json
index 2525026d17..7056fdc9fe 100644
--- a/datasets/SNEX20_GM_CSU_GPR_RAW_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_GM_CTSM_1.json b/datasets/SNEX20_GM_CTSM_1.json
index ee3555b754..13069f1c37 100644
--- a/datasets/SNEX20_GM_CTSM_1.json
+++ b/datasets/SNEX20_GM_CTSM_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX20_GM_Lidar_1.json b/datasets/SNEX20_GM_Lidar_1.json
index 717e8c5640..53037bc330 100644
--- a/datasets/SNEX20_GM_Lidar_1.json
+++ b/datasets/SNEX20_GM_Lidar_1.json
@@ -42,7 +42,7 @@
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index 14ac66c08f..2c3d648c42 100644
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index 7b4647e83f..0bb9fe7813 100644
--- a/datasets/SNEX20_GM_SWE_SD_1.json
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index 5220f8e40c..5f3e61bd0b 100644
--- a/datasets/SNEX20_J_UNM_GPR_1.json
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index ace10b9a79..d9d6c84ae5 100644
--- a/datasets/SNEX20_QSI_DEM_1.json
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index 81fce951bd..9f7446eaf1 100644
--- a/datasets/SNEX20_QSI_DEM_3m_1.json
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diff --git a/datasets/SNEX20_QSI_SD_1.json b/datasets/SNEX20_QSI_SD_1.json
index 8c8eacfef9..a19f2adb67 100644
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index 37b11aa27e..ef1bc6b4eb 100644
--- a/datasets/SNEX20_QSI_SD_3m_1.json
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index 98d4824261..69331fe786 100644
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index 7f53a03821..abdb38c9b3 100644
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index 6fd4ecb16b..47f80cec7d 100644
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index c2f7c3d46d..7f1374bd63 100644
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index c6d10515b9..683350cd59 100644
--- a/datasets/SNEX20_SMP_1.json
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index 241a6b219c..74f180a96e 100644
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diff --git a/datasets/SNEX20_SWESARR_TB_1.json b/datasets/SNEX20_SWESARR_TB_1.json
index 1e008abc2c..14a91ac265 100644
--- a/datasets/SNEX20_SWESARR_TB_1.json
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diff --git a/datasets/SNEX20_TLI_1.json b/datasets/SNEX20_TLI_1.json
index 2be88b361a..d33f464f19 100644
--- a/datasets/SNEX20_TLI_1.json
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diff --git a/datasets/SNEX20_TLS_PC_BSU_1.json b/datasets/SNEX20_TLS_PC_BSU_1.json
index 4fb7da5ad5..dec08be33a 100644
--- a/datasets/SNEX20_TLS_PC_BSU_1.json
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index 34f7294243..88c80beb39 100644
--- a/datasets/SNEX20_TLS_PC_BSU_RAW_1.json
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index ee1d14fcc6..83dcd0a24a 100644
--- a/datasets/SNEX20_TLS_PC_CRREL_1.json
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index 9a7ae56f6b..82cd03d147 100644
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index 2037700d61..d11f85c92c 100644
--- a/datasets/SNEX20_UNM_GPR_1.json
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index f18a686d03..1f7fb91f2c 100644
--- a/datasets/SNEX20_VPTS_Raw_1.json
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index 9d638cb323..e77c78d241 100644
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index 78b5b1d6f1..96016ffa34 100644
--- a/datasets/SNEX21_DSM_1.json
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index 383e9a8f89..9976efa9e9 100644
--- a/datasets/SNEX21_PS_DSM_1.json
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index 6843108676..fdd98970c2 100644
--- a/datasets/SNEX21_PS_MET_1.json
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index 5504cb7d47..8a2427ef2a 100644
--- a/datasets/SNEX21_SSR_1.json
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diff --git a/datasets/SNEX21_TS_SP_1.json b/datasets/SNEX21_TS_SP_1.json
index 87d3e125d6..5528abe9e6 100644
--- a/datasets/SNEX21_TS_SP_1.json
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diff --git a/datasets/SNEX23_BCEF_TLS_1.json b/datasets/SNEX23_BCEF_TLS_1.json
index b0fd0dbdf0..32fbfda2b1 100644
--- a/datasets/SNEX23_BCEF_TLS_1.json
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index da5babba87..062feb1de3 100644
--- a/datasets/SNEX23_BCEF_TLS_Raw_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX23_CRREL_GPR_1.json b/datasets/SNEX23_CRREL_GPR_1.json
index dfba8be703..07dd64ef7f 100644
--- a/datasets/SNEX23_CRREL_GPR_1.json
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@@ -42,7 +42,7 @@
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index 09f73c743d..b1ae76b029 100644
--- a/datasets/SNEX23_CRREL_GPR_Raw_1.json
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index 0f54285e97..8b077f1655 100644
--- a/datasets/SNEX23_CSU_GPR_Raw_1.json
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diff --git a/datasets/SNEX23_Lidar_1.json b/datasets/SNEX23_Lidar_1.json
index 206f48a6e2..240854538e 100644
--- a/datasets/SNEX23_Lidar_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX23_Lidar_Raw_1.json b/datasets/SNEX23_Lidar_Raw_1.json
index 36655cf3a4..942ecd5beb 100644
--- a/datasets/SNEX23_Lidar_Raw_1.json
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@@ -42,7 +42,7 @@
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index 5b7a8ea9e9..135b56b362 100644
--- a/datasets/SNEX23_MAR22_SD_1.json
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index 7bee407363..058b6e577e 100644
--- a/datasets/SNEX23_MAR23_SD_1.json
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index 336ee93291..5ae77416fc 100644
--- a/datasets/SNEX23_SSA_1.json
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diff --git a/datasets/SNEX23_SSA_SO_1.json b/datasets/SNEX23_SSA_SO_1.json
index e561e02e52..ee0109c35f 100644
--- a/datasets/SNEX23_SSA_SO_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX23_SWE_1.json b/datasets/SNEX23_SWE_1.json
index 6700393717..20b31a16db 100644
--- a/datasets/SNEX23_SWE_1.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SNEX23_UW_GPR_1.json b/datasets/SNEX23_UW_GPR_1.json
index 81bbd5ed6e..caa8f1d8d1 100644
--- a/datasets/SNEX23_UW_GPR_1.json
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@@ -42,7 +42,7 @@
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         }
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diff --git a/datasets/SNEX_MCS_Lidar_1.json b/datasets/SNEX_MCS_Lidar_1.json
new file mode 100644
index 0000000000..f6edf7b4d1
--- /dev/null
+++ b/datasets/SNEX_MCS_Lidar_1.json
@@ -0,0 +1,130 @@
+{
+    "type": "Collection",
+    "id": "SNEX_MCS_Lidar_1",
+    "stac_version": "1.0.0",
+    "description": "The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as  <a href=\"https://nsidc.org/data/SNEX_MCS_Lidar_Raw\">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1</a>) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.",
+    "links": [
+        {
+            "rel": "license",
+            "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy",
+            "type": "text/html",
+            "title": "EOSDIS Data Use Policy"
+        },
+        {
+            "rel": "about",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.html",
+            "type": "text/html",
+            "title": "HTML metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.native",
+            "type": "application/xml",
+            "title": "Native metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.echo10",
+            "type": "application/echo10+xml",
+            "title": "ECHO10 metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.json",
+            "type": "application/json",
+            "title": "CMR JSON metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.umm_json",
+            "type": "application/vnd.nasa.cmr.umm+json",
+            "title": "CMR UMM_JSON metadata for collection"
+        },
+        {
+            "rel": "self",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_1",
+            "type": "application/json"
+        },
+        {
+            "rel": "root",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS",
+            "type": "application/json",
+            "title": "NSIDC_ECS STAC Catalog"
+        },
+        {
+            "rel": "items",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_1/items",
+            "type": "application/json"
+        }
+    ],
+    "provider": [
+        {
+            "name": "NSIDC_ECS",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "NASA EOSDIS",
+            "roles": [
+                "host"
+            ]
+        }
+    ],
+    "title": "SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey V001",
+    "extent": {
+        "spatial": {
+            "bbox": [
+                [
+                    -115.7304987,
+                    43.9136453,
+                    -115.6424377,
+                    43.9748021
+                ]
+            ]
+        },
+        "temporal": {
+            "interval": [
+                [
+                    "2021-03-10T00:00:00Z",
+                    "2024-04-18T23:59:59.999000Z"
+                ]
+            ]
+        }
+    },
+    "license": "proprietary",
+    "assets": {
+        "org/SNOWEX/SNEX_MCS_Lidar": {
+            "href": "https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX_MCS_Lidar.001/",
+            "title": "Direct Download [3]",
+            "description": "Direct download via HTTPS protocol.",
+            "roles": [
+                "data"
+            ]
+        },
+        "nasa": {
+            "href": "https://search.earthdata.nasa.gov/search?q=SNEX_MCS_Lidar+V001",
+            "title": "Direct Download [4]",
+            "description": "NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.",
+            "roles": [
+                "data"
+            ]
+        },
+        "https://nsidc": {
+            "href": "https://nsidc.org/data/data-access-tool/SNEX_MCS_Lidar/versions/1/",
+            "title": "Direct Download [5]",
+            "description": "Search and filter data files using a map-based interface",
+            "roles": [
+                "data"
+            ]
+        },
+        "provider_metadata": {
+            "href": "https://doi.org/10.5067/NE8CLN3ES5XQ",
+            "title": "Provider Metadata",
+            "description": "Provides access to data, documentation, tools, citation information, support, and other resources.",
+            "roles": [
+                "metadata"
+            ]
+        }
+    }
+}
\ No newline at end of file
diff --git a/datasets/SNEX_MCS_Lidar_Raw_1.json b/datasets/SNEX_MCS_Lidar_Raw_1.json
new file mode 100644
index 0000000000..2c99d2b67d
--- /dev/null
+++ b/datasets/SNEX_MCS_Lidar_Raw_1.json
@@ -0,0 +1,130 @@
+{
+    "type": "Collection",
+    "id": "SNEX_MCS_Lidar_Raw_1",
+    "stac_version": "1.0.0",
+    "description": "The data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign. \n\nDigital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as <a href=\"https://nsidc.org/data/SNEX_MCS_Lidar\">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1</a>.",
+    "links": [
+        {
+            "rel": "license",
+            "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy",
+            "type": "text/html",
+            "title": "EOSDIS Data Use Policy"
+        },
+        {
+            "rel": "about",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.html",
+            "type": "text/html",
+            "title": "HTML metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.native",
+            "type": "application/xml",
+            "title": "Native metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.echo10",
+            "type": "application/echo10+xml",
+            "title": "ECHO10 metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.json",
+            "type": "application/json",
+            "title": "CMR JSON metadata for collection"
+        },
+        {
+            "rel": "via",
+            "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.umm_json",
+            "type": "application/vnd.nasa.cmr.umm+json",
+            "title": "CMR UMM_JSON metadata for collection"
+        },
+        {
+            "rel": "self",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_Raw_1",
+            "type": "application/json"
+        },
+        {
+            "rel": "root",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS",
+            "type": "application/json",
+            "title": "NSIDC_ECS STAC Catalog"
+        },
+        {
+            "rel": "items",
+            "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_Raw_1/items",
+            "type": "application/json"
+        }
+    ],
+    "provider": [
+        {
+            "name": "NSIDC_ECS",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "NASA EOSDIS",
+            "roles": [
+                "host"
+            ]
+        }
+    ],
+    "title": "SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw V001",
+    "extent": {
+        "spatial": {
+            "bbox": [
+                [
+                    -115.7304987,
+                    43.9136453,
+                    -115.6424377,
+                    43.9748021
+                ]
+            ]
+        },
+        "temporal": {
+            "interval": [
+                [
+                    "2021-03-10T00:00:00Z",
+                    "2024-04-18T23:59:59.999000Z"
+                ]
+            ]
+        }
+    },
+    "license": "proprietary",
+    "assets": {
+        "org/SNOWEX/SNEX_MCS_Lidar_Raw": {
+            "href": "https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX_MCS_Lidar_Raw.001/",
+            "title": "Direct Download [0]",
+            "description": "Direct download via HTTPS protocol.",
+            "roles": [
+                "data"
+            ]
+        },
+        "nasa": {
+            "href": "https://search.earthdata.nasa.gov/search?q=SNEX_MCS_Lidar_Raw+V001",
+            "title": "Direct Download [1]",
+            "description": "NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.",
+            "roles": [
+                "data"
+            ]
+        },
+        "https://nsidc": {
+            "href": "https://nsidc.org/data/data-access-tool/SNEX_MCS_Lidar_Raw/versions/1/",
+            "title": "Direct Download [2]",
+            "description": "Search and filter data files using a map-based interface",
+            "roles": [
+                "data"
+            ]
+        },
+        "provider_metadata": {
+            "href": "https://doi.org/10.5067/OYF98UGSOUQY",
+            "title": "Provider Metadata",
+            "description": "Provides access to data, documentation, tools, citation information, support, and other resources.",
+            "roles": [
+                "metadata"
+            ]
+        }
+    }
+}
\ No newline at end of file
diff --git a/datasets/SPURS1_ADCP_1.0.json b/datasets/SPURS1_ADCP_1.0.json
index 6f3da685ad..4847d85585 100644
--- a/datasets/SPURS1_ADCP_1.0.json
+++ b/datasets/SPURS1_ADCP_1.0.json
@@ -42,7 +42,7 @@
         },
         {
             "rel": "self",
-            "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic2VudGluZWwtNmEgbWYgamFzb24tY3MgbDIgcDQgYWx0aW1ldGVyIGxvdyByZXNvbHV0aW9uIChscikgbnJ0IHJlZHVjZWQgb2NlYW4gc3VyZmFjZSB0b3BvZ3JhcGh5XCIsXCJQT0NMT1VEXCIsXCJKQVNPTl9DU19TNkFfTDJfQUxUX0xSX1JFRF9PU1RfTlJUX0ZcIixcImZcIiwxOTY4OTgwNTc2LDIwXSIsInVtbSI6IltcInNlbnRpbmVsLTZhIG1mIGphc29uLWNzIGwyIHA0IGFsdGltZXRlciBsb3cgcmVzb2x1dGlvbiAobHIpIG5ydCByZWR1Y2VkIG9jZWFuIHN1cmZhY2UgdG9wb2dyYXBoeVwiLFwiUE9DTE9VRFwiLFwiSkFTT05fQ1NfUzZBX0wyX0FMVF9MUl9SRURfT1NUX05SVF9GXCIsXCJmXCIsMTk2ODk4MDU3NiwyMF0ifQ%3D%3D/SPURS1_ADCP_1.0",
+            "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic2VudGluZWwtNmEgbWYgamFzb24tY3MgbDJwIHA0IGFsdGltZXRlciBoaWdoIHJlc29sdXRpb24gKGhyKSBudGMgb2NlYW4gc3VyZmFjZSB0b3BvZ3JhcGh5IGYwOFwiLFwiUE9DTE9VRFwiLFwiSkFTT05fQ1NfUzZBX0wyUF9BTFRfSFJfT1NUX05UQ19GMDhcIixcImYwOFwiLDI2MTk0NDM5MTEsMTBdIiwidW1tIjoiW1wic2VudGluZWwtNmEgbWYgamFzb24tY3MgbDJwIHA0IGFsdGltZXRlciBoaWdoIHJlc29sdXRpb24gKGhyKSBudGMgb2NlYW4gc3VyZmFjZSB0b3BvZ3JhcGh5IGYwOFwiLFwiUE9DTE9VRFwiLFwiSkFTT05fQ1NfUzZBX0wyUF9BTFRfSFJfT1NUX05UQ19GMDhcIixcImYwOFwiLDI2MTk0NDM5MTEsMTBdIn0%3D/SPURS1_ADCP_1.0",
             "type": "application/json"
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@@ -53,7 +53,7 @@
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+            "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic2VudGluZWwtNmEgbWYgamFzb24tY3MgbDJwIHA0IGFsdGltZXRlciBoaWdoIHJlc29sdXRpb24gKGhyKSBudGMgb2NlYW4gc3VyZmFjZSB0b3BvZ3JhcGh5IGYwOFwiLFwiUE9DTE9VRFwiLFwiSkFTT05fQ1NfUzZBX0wyUF9BTFRfSFJfT1NUX05UQ19GMDhcIixcImYwOFwiLDI2MTk0NDM5MTEsMTBdIiwidW1tIjoiW1wic2VudGluZWwtNmEgbWYgamFzb24tY3MgbDJwIHA0IGFsdGltZXRlciBoaWdoIHJlc29sdXRpb24gKGhyKSBudGMgb2NlYW4gc3VyZmFjZSB0b3BvZ3JhcGh5IGYwOFwiLFwiUE9DTE9VRFwiLFwiSkFTT05fQ1NfUzZBX0wyUF9BTFRfSFJfT1NUX05UQ19GMDhcIixcImYwOFwiLDI2MTk0NDM5MTEsMTBdIn0%3D/SPURS1_ADCP_1.0/items",
             "type": "application/json"
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diff --git a/datasets/SPURS1_CTD_1.0.json b/datasets/SPURS1_CTD_1.0.json
index e69f87d5ba..e2af486780 100644
--- a/datasets/SPURS1_CTD_1.0.json
+++ b/datasets/SPURS1_CTD_1.0.json
@@ -42,7 +42,7 @@
         },
         {
             "rel": "self",
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index 5f89671c30..52516d7396 100644
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index 371fe8362a..4dd97a08a9 100644
--- a/datasets/SPURS1_ECOMAPPER_1.0.json
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index 0cecba72a1..d4a644a337 100644
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index ff1a727bc5..854e2ef994 100644
--- a/datasets/SPURS1_MOORING_PICO_1.0.json
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index 95e4e37a3d..6c08922167 100644
--- a/datasets/SPURS1_MOORING_WHOI_1.0.json
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index 961e28e8d0..557e7a5031 100644
--- a/datasets/SPURS1_SEAGLIDER_1.0.json
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diff --git a/datasets/SPURS1_SEASOAR_1.0.json b/datasets/SPURS1_SEASOAR_1.0.json
index 9d652fc830..abeddfe923 100644
--- a/datasets/SPURS1_SEASOAR_1.0.json
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index 17f774f3ab..c57d66cd29 100644
--- a/datasets/SPURS1_TENUSEGLIDER_1.0.json
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diff --git a/datasets/SPURS1_TSG_1.0.json b/datasets/SPURS1_TSG_1.0.json
index ce249843a0..1c7275c4ad 100644
--- a/datasets/SPURS1_TSG_1.0.json
+++ b/datasets/SPURS1_TSG_1.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS1_UCTD_1.0.json b/datasets/SPURS1_UCTD_1.0.json
index a0927bf287..95c1852932 100644
--- a/datasets/SPURS1_UCTD_1.0.json
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@@ -42,7 +42,7 @@
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index f003447bb3..29b5b675e7 100644
--- a/datasets/SPURS1_WAVEGLIDER_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_ADCP_1.0.json b/datasets/SPURS2_ADCP_1.0.json
index c67780640f..4b80ca54ce 100644
--- a/datasets/SPURS2_ADCP_1.0.json
+++ b/datasets/SPURS2_ADCP_1.0.json
@@ -42,7 +42,7 @@
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index 3bbd206c82..b4e3982883 100644
--- a/datasets/SPURS2_ARGO_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_CFT_1.0.json b/datasets/SPURS2_CFT_1.0.json
index cc37533c47..48e942f801 100644
--- a/datasets/SPURS2_CFT_1.0.json
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diff --git a/datasets/SPURS2_CTD_1.0.json b/datasets/SPURS2_CTD_1.0.json
index 258dd47256..8db433f32c 100644
--- a/datasets/SPURS2_CTD_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_DISDR_1.0.json b/datasets/SPURS2_DISDR_1.0.json
index 60e42d1413..cc2198f449 100644
--- a/datasets/SPURS2_DISDR_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_DRIFTER_1.0.json b/datasets/SPURS2_DRIFTER_1.0.json
index 0345c6ae58..03356f1e37 100644
--- a/datasets/SPURS2_DRIFTER_1.0.json
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index f0ea1ba111..59d39dbe67 100644
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index ab795153bf..0b9bce59f9 100644
--- a/datasets/SPURS2_LADYAMBER_1.0.json
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index ee40cab7c0..0da28381c4 100644
--- a/datasets/SPURS2_METEO_1.0.json
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index 32f52fd3e9..2ca148c22b 100644
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index c4b0807d8a..5526c65f7d 100644
--- a/datasets/SPURS2_MOORING_PICO_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_PALS_1.0.json b/datasets/SPURS2_PALS_1.0.json
index 2a1b5fe6dd..75a153900e 100644
--- a/datasets/SPURS2_PALS_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SPURS2_RAINRADAR_1.0.json b/datasets/SPURS2_RAINRADAR_1.0.json
index 8e07d84c35..7b0e417f30 100644
--- a/datasets/SPURS2_RAINRADAR_1.0.json
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diff --git a/datasets/SPURS2_RAWINSONDE_1.0.json b/datasets/SPURS2_RAWINSONDE_1.0.json
index fe7bbf4bd1..6934c2251e 100644
--- a/datasets/SPURS2_RAWINSONDE_1.0.json
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diff --git a/datasets/SPURS2_SAILDRONE_1.0.json b/datasets/SPURS2_SAILDRONE_1.0.json
index 4a82207e70..9e600d9bf7 100644
--- a/datasets/SPURS2_SAILDRONE_1.0.json
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index cb1f10bbbe..7e86deb440 100644
--- a/datasets/SPURS2_SALINITYSNAKE_1.0.json
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index 1d49219430..2b2dd148f4 100644
--- a/datasets/SPURS2_SEAGLIDER_1.0.json
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index dfbf628110..a2157de99f 100644
--- a/datasets/SPURS2_SSP_1.0.json
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index 617a96be4b..c346577ae6 100644
--- a/datasets/SPURS2_UCTD_1.0.json
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index 6ab84124ca..c90544ac7e 100644
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index da3c93c919..cd6521b204 100644
--- a/datasets/SPURS2_USPS_1.0.json
+++ b/datasets/SPURS2_USPS_1.0.json
@@ -42,7 +42,7 @@
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index 5143030d92..98df17b4d3 100644
--- a/datasets/SPURS2_WAMOS_1.0.json
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diff --git a/datasets/SPURS2_WAVEGLIDER_1.0.json b/datasets/SPURS2_WAVEGLIDER_1.0.json
index 16641bc66d..792348ae5c 100644
--- a/datasets/SPURS2_WAVEGLIDER_1.0.json
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@@ -42,7 +42,7 @@
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index 3cfe1ff0a8..c31ef55a6f 100644
--- a/datasets/SPURS2_XBAND_1.0.json
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@@ -42,7 +42,7 @@
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index 33cf43bcd3..4f8433953e 100644
--- a/datasets/SPURS2_XBAND_IMG_1.0.json
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index 1c1d88a63b..86076dc85a 100644
--- a/datasets/SPURS2_XBT_1.0.json
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@@ -42,7 +42,7 @@
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index 27f8ca0b58..d11b9140c3 100644
--- a/datasets/SRTMGL1N_003.json
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index fcc8b8b5b6..d4a6908b54 100644
--- a/datasets/SRTMGL1_003.json
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index cbc3ceee74..29cadac4bc 100644
--- a/datasets/SRTMGL1_NC_003.json
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index 92ed8d641b..026e5cbe74 100644
--- a/datasets/SRTMGL1_NUMNC_003.json
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diff --git a/datasets/SRTMGL30_002.json b/datasets/SRTMGL30_002.json
index b4d6649ff5..d1a5c6c83b 100644
--- a/datasets/SRTMGL30_002.json
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@@ -42,7 +42,7 @@
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index 6fbd6fa974..5083974e2b 100644
--- a/datasets/SWOT_L1B_LR_INTF_1.0_1.0.json
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index 912cc7093b..9f5cab328a 100644
--- a/datasets/SWOT_L1B_LR_INTF_1.1_1.1.json
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index ad01354375..60cccb16b6 100644
--- a/datasets/SWOT_L1B_LR_INTF_2.0_2.0.json
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index 70c8c98c57..39aa98fb01 100644
--- a/datasets/SWOT_L1_DORIS_RINEX_1.0_1.0.json
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@@ -42,7 +42,7 @@
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index cb9b6225bb..1fed3cba3c 100644
--- a/datasets/SWOT_L1_GPSP_RINEX_1.0_1.0.json
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@@ -42,7 +42,7 @@
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index faf735eefe..92d0467a59 100644
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index b741319680..ce1b4a0d26 100644
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index 02579452a7..2a63bdbc78 100644
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index 1704f1bfb3..2dc8efcc50 100644
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index 9abf8d9c8c..d1a08e9fed 100644
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index 69a9b8024c..de10ea79a3 100644
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index 4ab67ab836..1434224164 100644
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index dc7e547fcc..e7ce3432c4 100644
--- a/datasets/SWOT_L2_HR_Raster_1.1_1.1.json
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index 6dba00d7e4..2766862949 100644
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index 4c93d8311b..6cba4f19ef 100644
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index 551bd01429..58b3889928 100644
--- a/datasets/SWOT_L2_HR_Raster_250m_2.0_2.0.json
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index 4e3d2a4bcc..361cb514fa 100644
--- a/datasets/SWOT_L2_HR_RiverAvg_2.0_2.0.json
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index 37ea42972c..649f87e244 100644
--- a/datasets/SWOT_L2_HR_RiverSP_1.1_1.1.json
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index 61ad78927e..29f50ef44a 100644
--- a/datasets/SWOT_L2_HR_RiverSP_2.0_2.0.json
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diff --git a/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json b/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json
index 7461e4b4f0..16d1052e81 100644
--- a/datasets/SWOT_L2_HR_RiverSP_node_2.0_2.0.json
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@@ -42,7 +42,7 @@
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index cb463a649f..f1ab24e7be 100644
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index 22174ccb5a..ed6a26386a 100644
--- a/datasets/SWOT_L2_LR_SSH_1.0_1.0.json
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index 7b7c7fc533..9a1b8346a6 100644
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index f33eb57fb4..410a80209d 100644
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index f1897550a0..29904ca0e7 100644
--- a/datasets/SWOT_L2_LR_SSH_BASIC_2.0_2.0.json
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@@ -42,7 +42,7 @@
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index d1b488ee2a..8f2f4240a8 100644
--- a/datasets/SWOT_L2_LR_SSH_EXPERT_2.0_2.0.json
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index 86b669fe64..47dc704efe 100644
--- a/datasets/SWOT_L2_LR_SSH_UNSMOOTHED_2.0_2.0.json
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index 1bd6242624..6b2faa3744 100644
--- a/datasets/SWOT_L2_LR_SSH_WINDWAVE_2.0_2.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json
index ed127d21de..9c88b5d4fe 100644
--- a/datasets/SWOT_L2_NALT_GDR_2.0_2.0.json
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@@ -42,7 +42,7 @@
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index 660360faba..a0c28e4bbe 100644
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index 808f4bb40b..2b8ce1d9de 100644
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index d1f1483cb7..2b823c0e0b 100644
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index 29b4abd48e..e5b669c242 100644
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diff --git a/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_2.0_2.0.json
index 651f77b1c6..15645e82e0 100644
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index 7fe43ec1c1..eaa39810a4 100644
--- a/datasets/SWOT_L2_NALT_IGDR_GDR_1.0_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json b/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json
index 9b66627206..f8c6b24513 100644
--- a/datasets/SWOT_L2_NALT_IGDR_GDR_2.0_2.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json
index 06b1c11e88..a102e0d029 100644
--- a/datasets/SWOT_L2_NALT_IGDR_SGDR_1.0_1.0.json
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@@ -42,7 +42,7 @@
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index 1752264ba2..261a8e1b59 100644
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index 093740f13a..7595ae4adb 100644
--- a/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json
+++ b/datasets/SWOT_L2_NALT_IGDR_SSHA_1.0_1.0.json
@@ -42,7 +42,7 @@
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index ef1854c067..5a96b2e26e 100644
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json b/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json
index eb1f02bfc6..7aad34db0e 100644
--- a/datasets/SWOT_L2_NALT_OGDR_1.0_1.0.json
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@@ -42,7 +42,7 @@
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index 54dee429ea..4906f4dd5f 100644
--- a/datasets/SWOT_L2_NALT_OGDR_2.0_2.0.json
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index 4e5f3d2a59..ea6efc458c 100644
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index 9f18f90fa1..1219c15627 100644
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+++ b/datasets/SWOT_L2_NALT_OGDR_GDR_2.0_2.0.json
@@ -42,7 +42,7 @@
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index 277d74bd8f..6a233faaf0 100644
--- a/datasets/SWOT_L2_NALT_OGDR_SSHA_1.0_1.0.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json b/datasets/SWOT_L2_NALT_OGDR_SSHA_2.0_2.0.json
index 987d6a5c49..7d0f6bc56e 100644
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diff --git a/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json b/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json
index 1b6669f41a..32e217e126 100644
--- a/datasets/SWOT_L2_RAD_GDR_2.0_2.0.json
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diff --git a/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json b/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json
index 353f818f0e..71f56306f2 100644
--- a/datasets/SWOT_L2_RAD_IGDR_1.0_1.0.json
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index 5281ce9eb9..bcecac6c7e 100644
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index ce66a68451..5b85b75d6f 100644
--- a/datasets/SWOT_L2_RAD_OGDR_1.0_1.0.json
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index 3f401761c6..3e2bbec1ff 100644
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index 82ebca03e2..1ab2375d65 100644
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index ee4eeabe2b..b6cdbba340 100644
--- a/datasets/SWOT_L4_DAWG_SOS_DISCHARGE_1.json
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index 18b0d86bb8..82ee8314f1 100644
--- a/datasets/SWOT_MOE_1.0_1.0.json
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index c0550289d0..af0510ba1f 100644
--- a/datasets/SWOT_POE_2.0_2.0.json
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index b771b41bed..7d6983cd86 100644
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index b41d4c6eb1..c3f49daafa 100644
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index e349fc7485..5200789c0b 100644
--- a/datasets/SWOT_PRELAUNCH_L2_GPS_V1_1.0.json
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index 130de8326a..e80c156ef4 100644
--- a/datasets/SWOT_PRELAUNCH_L2_PIES_V1_1.0.json
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index 73ecc3bd82..0ef9b8d334 100644
--- a/datasets/SWOT_PRELAUNCH_L2_PRAWLER_V1_1.0.json
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index ae1526d04c..08fdcba3ca 100644
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index 6d0f90b8e0..fa6d3c8a29 100644
--- a/datasets/SWOT_PRELAUNCH_L2_WHOICTD_V1_1.0.json
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diff --git a/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json b/datasets/SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1_1.json
index d0b87c96fb..ed1de8a104 100644
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index 260a6351d0..728aefec05 100644
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index 50f9f0d893..6bc6867da6 100644
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index aaa7db7afc..b6d57532ff 100644
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index 4f8e5441ed..b11253399a 100644
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index dc9c880318..253282e9e7 100644
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index 264fd93826..fbac57a7a7 100644
--- a/datasets/Semantic Segmentation of Crop Type in Ghana_1.json	
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diff --git a/datasets/Smallholder Cashew Plantations in Benin_1.json b/datasets/Smallholder Cashew Plantations in Benin_1.json
index 8650feb388..0cabd39170 100644
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diff --git a/datasets/South Africa Crop Type Competition_1.json b/datasets/South Africa Crop Type Competition_1.json
index 44304e177f..8dff261cf3 100644
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index 1d36f35ceb..118da2e99c 100644
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index cfee266176..30b8c2b204 100644
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index 405acb21a7..ccff19d3bf 100644
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index 79e0fe7527..283c97e970 100644
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index ed2f42cbfd..9bd988542a 100644
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index 99ec32b24d..0022219b2c 100644
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index 55fe90487e..18765700b2 100644
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index cd317bbb69..68843cc212 100644
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index 34d919bab7..b64a16875c 100644
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index 85ce8864bd..b5314dacd0 100644
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index a327aa6314..1648c5af01 100644
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index 1cc75842ec..980c857196 100644
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index 919e1215b4..f801ef9d3b 100644
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diff --git a/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04.json
index efce9fd9f3..316c9de6f9 100644
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diff --git a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04.json
index 297863dbbb..ff0049807b 100644
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diff --git a/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json b/datasets/TELLUS_GRFO_L3_JPL_RL06.3_OCN_v04_RL06.3v04.json
index b308b05d8c..f36974b03d 100644
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index c63a94cf67..2fe39e27e8 100644
--- a/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json
+++ b/datasets/TEMPEST_STPH8_L1_TSDR_V10.0_10.0.json
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index d689647923..e469d4c55a 100644
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index 2bc385b0ea..663629aaa1 100644
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diff --git a/datasets/TOPEX_ALTSDR_A.json b/datasets/TOPEX_ALTSDR_A.json
index dcd5079ab7..78f7a8e23e 100644
--- a/datasets/TOPEX_ALTSDR_A.json
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index 1dd599841a..d2d2fb2ba8 100644
--- a/datasets/TOPEX_POSEIDON_GDR_F_F.json
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index aadaeb6321..f624daab6e 100644
--- a/datasets/Tropical Cyclone Wind Estimation Competition_1.json	
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diff --git a/datasets/UAVSAR_INSAR_INT_1.json b/datasets/UAVSAR_INSAR_INT_1.json
index 5aa1345b41..3135b4dd17 100644
--- a/datasets/UAVSAR_INSAR_INT_1.json
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diff --git a/datasets/UAVSAR_INSAR_INT_GRD_1.json b/datasets/UAVSAR_INSAR_INT_GRD_1.json
index 76caa2e1f9..fd819b22b4 100644
--- a/datasets/UAVSAR_INSAR_INT_GRD_1.json
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diff --git a/datasets/UAVSAR_INSAR_KMZ_1.json b/datasets/UAVSAR_INSAR_KMZ_1.json
index 482b011b5f..a4bcb8dbe3 100644
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diff --git a/datasets/UAVSAR_INSAR_META_1.json b/datasets/UAVSAR_INSAR_META_1.json
index ec9884d007..e2c9f4f7b6 100644
--- a/datasets/UAVSAR_INSAR_META_1.json
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index db23508481..37904c755a 100644
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index d50fc52003..342a5cc7a3 100644
--- a/datasets/UAVSAR_POL_ML_CMPLX_SLANT_1.json
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index ce24f55ab3..8c26254d27 100644
--- a/datasets/UAVSAR_POL_PAULI_1.json
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@@ -42,7 +42,7 @@
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index e7d4e01f21..8dbbdddbdb 100644
--- a/datasets/UAVSAR_POL_SLOPE_1.json
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index f9148f261e..e10a40c951 100644
--- a/datasets/UAVSAR_POL_STOKES_1.json
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index 534822f67b..a2732bfe95 100644
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diff --git a/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json b/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json
index 0d687cccab..f8106cb57e 100644
--- a/datasets/VIIRS_N20-NAVO-L2P-v3.0_3.0.json
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@@ -42,7 +42,7 @@
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index 671f161f3e..9dc17629df 100644
--- a/datasets/VIIRS_N20-STAR-L2P-v2.80_2.80.json
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index e5919ee2fb..395c26dd52 100644
--- a/datasets/VIIRS_N20-STAR-L3U-v2.80_2.80.json
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index b9a9846d49..b3b36fb866 100644
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diff --git a/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json b/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json
index f4ccae2748..e446cec502 100644
--- a/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json
+++ b/datasets/VIIRS_NPP-JPL-L2P-v2016.2_2016.2.json
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index af5359c0b8..00cff6b33a 100644
--- a/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json
+++ b/datasets/VIIRS_NPP-NAVO-L2P-v1.0_1.0.json
@@ -42,7 +42,7 @@
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index 3036384fc7..33d97f42ea 100644
--- a/datasets/VIIRS_NPP-NAVO-L2P-v2.0_2.0.json
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index d5745acaae..5b9034778a 100644
--- a/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json
+++ b/datasets/VIIRS_NPP-NAVO-L2P-v3.0_3.0.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json b/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json
index b3a01c8d95..e8a93105f6 100644
--- a/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json
+++ b/datasets/VIIRS_NPP-STAR-L2P-v2.80_2.80.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json b/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json
index 86f03f7d22..c905348ca8 100644
--- a/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json
+++ b/datasets/VIIRS_NPP-STAR-L3U-v2.80_2.80.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json b/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json
index a6b350c602..e647201a05 100644
--- a/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json
+++ b/datasets/VIIRS_SST_NPP_NAR-OSISAF-L3C-v1.0_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIP01_004.json b/datasets/VIP01_004.json
index d36b7f4b04..e73533f2fb 100644
--- a/datasets/VIP01_004.json
+++ b/datasets/VIP01_004.json
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diff --git a/datasets/VIP07_004.json b/datasets/VIP07_004.json
index c39166f323..e7e6d03279 100644
--- a/datasets/VIP07_004.json
+++ b/datasets/VIP07_004.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIP15_004.json b/datasets/VIP15_004.json
index b031d06767..6a08734f11 100644
--- a/datasets/VIP15_004.json
+++ b/datasets/VIP15_004.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VIP30_004.json b/datasets/VIP30_004.json
index 6a620d80e5..8f61e8c038 100644
--- a/datasets/VIP30_004.json
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diff --git a/datasets/VIPPHEN_EVI2_004.json b/datasets/VIPPHEN_EVI2_004.json
index 6d8f57d846..bf4e98e936 100644
--- a/datasets/VIPPHEN_EVI2_004.json
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diff --git a/datasets/VIPPHEN_NDVI_004.json b/datasets/VIPPHEN_NDVI_004.json
index e872469a7b..39210d4aa4 100644
--- a/datasets/VIPPHEN_NDVI_004.json
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index db56d21791..be6321a745 100644
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index 6fff6ac4fe..af11f9a874 100644
--- a/datasets/VJ130P1N_2.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VJ130_2.json b/datasets/VJ130_2.json
index 678632474f..48a07d9c84 100644
--- a/datasets/VJ130_2.json
+++ b/datasets/VJ130_2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VJ218A1_002.json b/datasets/VJ218A1_002.json
index 489b3b389c..5fbce8c67c 100644
--- a/datasets/VJ218A1_002.json
+++ b/datasets/VJ218A1_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VJ218A2_002.json b/datasets/VJ218A2_002.json
index 862cba9574..19fdad89b3 100644
--- a/datasets/VJ218A2_002.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VJ218C1_002.json b/datasets/VJ218C1_002.json
index 0808864607..f448842d9b 100644
--- a/datasets/VJ218C1_002.json
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@@ -42,7 +42,7 @@
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index ae645160bb..8ed31a13b5 100644
--- a/datasets/VJ218C2_002.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP03MODLL_001.json b/datasets/VNP03MODLL_001.json
index dd4c5c124c..48ab631222 100644
--- a/datasets/VNP03MODLL_001.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP03MODLL_002.json b/datasets/VNP03MODLL_002.json
index 2536b12855..10d8187922 100644
--- a/datasets/VNP03MODLL_002.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09A1_001.json b/datasets/VNP09A1_001.json
index 02c560896b..48bd181c83 100644
--- a/datasets/VNP09A1_001.json
+++ b/datasets/VNP09A1_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09A1_002.json b/datasets/VNP09A1_002.json
index 87ca0bdcf3..1f1c656c11 100644
--- a/datasets/VNP09A1_002.json
+++ b/datasets/VNP09A1_002.json
@@ -42,7 +42,7 @@
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index 286ce2fd76..5dad655a26 100644
--- a/datasets/VNP09CMG_001.json
+++ b/datasets/VNP09CMG_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09CMG_002.json b/datasets/VNP09CMG_002.json
index 03330343b0..1862c7f8c1 100644
--- a/datasets/VNP09CMG_002.json
+++ b/datasets/VNP09CMG_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09GA_001.json b/datasets/VNP09GA_001.json
index cbc5c84876..26cc393ea8 100644
--- a/datasets/VNP09GA_001.json
+++ b/datasets/VNP09GA_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09GA_002.json b/datasets/VNP09GA_002.json
index f2ba376d10..7c73dd42bf 100644
--- a/datasets/VNP09GA_002.json
+++ b/datasets/VNP09GA_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09H1_001.json b/datasets/VNP09H1_001.json
index 1915266a42..24dcccaca8 100644
--- a/datasets/VNP09H1_001.json
+++ b/datasets/VNP09H1_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP09H1_002.json b/datasets/VNP09H1_002.json
index 402d45d841..87078d3561 100644
--- a/datasets/VNP09H1_002.json
+++ b/datasets/VNP09H1_002.json
@@ -42,7 +42,7 @@
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index 8eb9203a84..51a8524a5d 100644
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index 65781af8bb..23ec5890b5 100644
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index 3825efb64e..a95ea56b5a 100644
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index f5e10d9d0e..cf6638b88b 100644
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index 01e77ab331..ab37a535d3 100644
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diff --git a/datasets/VNP10_1.json b/datasets/VNP10_1.json
index 784b5b3704..b9c6f771aa 100644
--- a/datasets/VNP10_1.json
+++ b/datasets/VNP10_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP10_2.json b/datasets/VNP10_2.json
index 85d54b203e..7406de9333 100644
--- a/datasets/VNP10_2.json
+++ b/datasets/VNP10_2.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13A1_001.json b/datasets/VNP13A1_001.json
index 2971f943f3..ccbc9f0f45 100644
--- a/datasets/VNP13A1_001.json
+++ b/datasets/VNP13A1_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13A1_002.json b/datasets/VNP13A1_002.json
index 2acc9b1f44..1fb4897a6e 100644
--- a/datasets/VNP13A1_002.json
+++ b/datasets/VNP13A1_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13A2_001.json b/datasets/VNP13A2_001.json
index bfc7ab8d2f..e574da6405 100644
--- a/datasets/VNP13A2_001.json
+++ b/datasets/VNP13A2_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13A2_002.json b/datasets/VNP13A2_002.json
index f8fd195dd6..2cec1507bf 100644
--- a/datasets/VNP13A2_002.json
+++ b/datasets/VNP13A2_002.json
@@ -42,7 +42,7 @@
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index 61a8454748..af663bb8e8 100644
--- a/datasets/VNP13A3_001.json
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@@ -42,7 +42,7 @@
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index 74b7d5d0af..27ccf36978 100644
--- a/datasets/VNP13A3_002.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13C1_001.json b/datasets/VNP13C1_001.json
index 6c8dd4d745..2fb651e3fa 100644
--- a/datasets/VNP13C1_001.json
+++ b/datasets/VNP13C1_001.json
@@ -42,7 +42,7 @@
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index e292bab3e7..aa63756d22 100644
--- a/datasets/VNP13C1_002.json
+++ b/datasets/VNP13C1_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP13C2_001.json b/datasets/VNP13C2_001.json
index c02b988b49..e955722b6f 100644
--- a/datasets/VNP13C2_001.json
+++ b/datasets/VNP13C2_001.json
@@ -42,7 +42,7 @@
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index 41b66bb9f9..ac9b17e345 100644
--- a/datasets/VNP13C2_002.json
+++ b/datasets/VNP13C2_002.json
@@ -42,7 +42,7 @@
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index d1958ba7df..ef1f530d84 100644
--- a/datasets/VNP14A1_001.json
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@@ -42,7 +42,7 @@
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index 12e89913b8..0847b68932 100644
--- a/datasets/VNP14A1_002.json
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@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP14IMG_002.json b/datasets/VNP14IMG_002.json
index c74b38486f..051e88a7ec 100644
--- a/datasets/VNP14IMG_002.json
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diff --git a/datasets/VNP14_001.json b/datasets/VNP14_001.json
index 7179cb5949..3b4c024851 100644
--- a/datasets/VNP14_001.json
+++ b/datasets/VNP14_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP14_002.json b/datasets/VNP14_002.json
index 11b45e3a98..193f4ac9f9 100644
--- a/datasets/VNP14_002.json
+++ b/datasets/VNP14_002.json
@@ -42,7 +42,7 @@
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index 61be8a8640..62b3094ee8 100644
--- a/datasets/VNP15A2H_001.json
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@@ -42,7 +42,7 @@
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index 3a6ea0db8d..6de97abbb0 100644
--- a/datasets/VNP15A2H_002.json
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index eaf2c08320..c45f51130c 100644
--- a/datasets/VNP21A1D_001.json
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index 585f984ca0..83c7823b61 100644
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diff --git a/datasets/VNP21A1N_001.json b/datasets/VNP21A1N_001.json
index 8b545e5dcc..6ced2eb411 100644
--- a/datasets/VNP21A1N_001.json
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index eaf04a4acb..0f8f9fcda7 100644
--- a/datasets/VNP21A1N_002.json
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index 3e4929c12e..96106a1f33 100644
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index c6fa8b4c20..84c0a9049b 100644
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index f28e917851..1a9ff9658b 100644
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index a400da4b40..90ab62fe50 100644
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index 4049b73f61..3f40d8ff2d 100644
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diff --git a/datasets/VNP21_001.json b/datasets/VNP21_001.json
index d8e31cc2ad..76eac62fd9 100644
--- a/datasets/VNP21_001.json
+++ b/datasets/VNP21_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP21_002.json b/datasets/VNP21_002.json
index f6679eeb3e..2b7783de79 100644
--- a/datasets/VNP21_002.json
+++ b/datasets/VNP21_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP22C2_001.json b/datasets/VNP22C2_001.json
index 3ce7c0a4d5..e9a1535ccd 100644
--- a/datasets/VNP22C2_001.json
+++ b/datasets/VNP22C2_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP22Q2_001.json b/datasets/VNP22Q2_001.json
index aa9a396f27..86edee3d13 100644
--- a/datasets/VNP22Q2_001.json
+++ b/datasets/VNP22Q2_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP28C2_002.json b/datasets/VNP28C2_002.json
index ca32276824..9017b6ca5d 100644
--- a/datasets/VNP28C2_002.json
+++ b/datasets/VNP28C2_002.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP28C3_002.json b/datasets/VNP28C3_002.json
index fd68fc6723..e295723f69 100644
--- a/datasets/VNP28C3_002.json
+++ b/datasets/VNP28C3_002.json
@@ -42,7 +42,7 @@
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index 0d008b2a15..8f52d4d93c 100644
--- a/datasets/VNP29P1D_2.json
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diff --git a/datasets/VNP29_1.json b/datasets/VNP29_1.json
index 61283588fb..20b6724e0c 100644
--- a/datasets/VNP29_1.json
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diff --git a/datasets/VNP29_2.json b/datasets/VNP29_2.json
index 0051fbd088..97d9b42c06 100644
--- a/datasets/VNP29_2.json
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diff --git a/datasets/VNP30P1D_2.json b/datasets/VNP30P1D_2.json
index e986712610..8e01141865 100644
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index 6ff6c094e6..132d53f2b5 100644
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index 9c1b3963f3..55d1cae04a 100644
--- a/datasets/VNP30_1.json
+++ b/datasets/VNP30_1.json
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index ce268e522c..c701606373 100644
--- a/datasets/VNP30_2.json
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index bd6d690afa..f06c3cbad9 100644
--- a/datasets/VNP43C1_001.json
+++ b/datasets/VNP43C1_001.json
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index 81ee9ed908..74fa984b58 100644
--- a/datasets/VNP43C2_001.json
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index efe83b963e..020d95d9fd 100644
--- a/datasets/VNP43C3_001.json
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diff --git a/datasets/VNP43C4_001.json b/datasets/VNP43C4_001.json
index bbfc9f51a2..03258e9e26 100644
--- a/datasets/VNP43C4_001.json
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@@ -42,7 +42,7 @@
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index 7418d586eb..e8cfb8472f 100644
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index f7a214768d..3a0efb83d4 100644
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index 991af7e3af..5c3af4e71d 100644
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index 89cb3cd4b6..82fdcbe4fc 100644
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index 7f65eb139f..a1635024bf 100644
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diff --git a/datasets/VNP43D64_001.json b/datasets/VNP43D64_001.json
index 5b287fe775..507b6d5c59 100644
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diff --git a/datasets/VNP43D65_001.json b/datasets/VNP43D65_001.json
index b36db6211f..0cbe992532 100644
--- a/datasets/VNP43D65_001.json
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index 4969fc112a..752bdd3311 100644
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index fef1ad0060..455719af8a 100644
--- a/datasets/VNP43D71_001.json
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index f063c12253..493b29d100 100644
--- a/datasets/VNP43D72_001.json
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index a73ddceb1c..9b52fd9278 100644
--- a/datasets/VNP43D73_001.json
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diff --git a/datasets/VNP43D74_001.json b/datasets/VNP43D74_001.json
index 0eba46839c..78659e3727 100644
--- a/datasets/VNP43D74_001.json
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diff --git a/datasets/VNP43D75_001.json b/datasets/VNP43D75_001.json
index 59e4873d9a..627af970aa 100644
--- a/datasets/VNP43D75_001.json
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index b94db7bbef..a24f68ce3f 100644
--- a/datasets/VNP43D76_001.json
+++ b/datasets/VNP43D76_001.json
@@ -42,7 +42,7 @@
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index 504d8ecba2..e7b85f89ca 100644
--- a/datasets/VNP43D77_001.json
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diff --git a/datasets/VNP43D78_001.json b/datasets/VNP43D78_001.json
index 06a9cf54b6..25b3f5c7a0 100644
--- a/datasets/VNP43D78_001.json
+++ b/datasets/VNP43D78_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP43D79_001.json b/datasets/VNP43D79_001.json
index e5cd02767f..6862b182bc 100644
--- a/datasets/VNP43D79_001.json
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diff --git a/datasets/VNP43D80_001.json b/datasets/VNP43D80_001.json
index eaa04aba48..877bb9c09c 100644
--- a/datasets/VNP43D80_001.json
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diff --git a/datasets/VNP43D81_001.json b/datasets/VNP43D81_001.json
index 89d895875c..eab40e933d 100644
--- a/datasets/VNP43D81_001.json
+++ b/datasets/VNP43D81_001.json
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index 99dd2ba647..a25ec4553f 100644
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index 302b7407e0..24f54a066b 100644
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diff --git a/datasets/VNP64A1_001.json b/datasets/VNP64A1_001.json
index cb3213bdc9..06694b40ee 100644
--- a/datasets/VNP64A1_001.json
+++ b/datasets/VNP64A1_001.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/VNP64A1_002.json b/datasets/VNP64A1_002.json
index ed91ccbe4d..5a6851a88e 100644
--- a/datasets/VNP64A1_002.json
+++ b/datasets/VNP64A1_002.json
@@ -2,7 +2,7 @@
     "type": "Collection",
     "id": "VNP64A1_002",
     "stac_version": "1.0.0",
-    "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. \n\nVNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.\n\nThe data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.\n\nNotification:  VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas.\n\n",
+    "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. \n\nThe data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.\n",
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+            "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP64A1.002/VNP64A1.A2024214.h09v04.002.2024277101833/BROWSE.VNP64A1.A2024214.h09v04.002.2024277101833.1.jpg",
             "type": "image/jpeg",
-            "title": "Download BROWSE.VNP64A1.A2016032.h08v05.002.2019340175445.1.jpg",
+            "title": "Download BROWSE.VNP64A1.A2024214.h09v04.002.2024277101833.1.jpg",
             "roles": [
                 "browse"
             ]
         },
         "thumbnail": {
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+            "href": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/VNP64A1.002/VNP64A1.A2024214.h09v04.002.2024277101833/BROWSE.VNP64A1.A2024214.h09v04.002.2024277101833.1.jpg",
             "title": "Thumbnail",
             "description": "Browse image for Earthdata Search",
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diff --git a/datasets/WAF_DEALIASED_SASS_L2_1.json b/datasets/WAF_DEALIASED_SASS_L2_1.json
index 78a763417e..f1f4aa58f7 100644
--- a/datasets/WAF_DEALIASED_SASS_L2_1.json
+++ b/datasets/WAF_DEALIASED_SASS_L2_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json b/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json
index dc3444007d..031b7a0abd 100644
--- a/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json
+++ b/datasets/WENTZ_NIMBUS-7_SMMR_L2_1.json
@@ -42,7 +42,7 @@
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diff --git a/datasets/WENTZ_SASS_SIGMA0_L2_1.json b/datasets/WENTZ_SASS_SIGMA0_L2_1.json
index 18f0e32018..5235e76b28 100644
--- a/datasets/WENTZ_SASS_SIGMA0_L2_1.json
+++ b/datasets/WENTZ_SASS_SIGMA0_L2_1.json
@@ -42,7 +42,7 @@
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index c96b871341..75024a843d 100644
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index 750ce396f3..153c967816 100644
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index f06e641ecc..72f751cc70 100644
--- a/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json	
+++ b/datasets/ramp Building Footprint Dataset - Accra, Ghana_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json
index aff65144c7..064459df6c 100644
--- a/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json	
+++ b/datasets/ramp Building Footprint Dataset - Barishal, Bangladesh_1.json	
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index 476b6113f8..77396aaaeb 100644
--- a/datasets/ramp Building Footprint Dataset - Bentiu, South Sudan_1.json	
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index dccb88e691..fcd4024820 100644
--- a/datasets/ramp Building Footprint Dataset - Chittagong, Bangladesh_1.json	
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index 2ac8196495..6a3c7c5309 100644
--- a/datasets/ramp Building Footprint Dataset - Cox's Bazar, Bangladesh_1.json	
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index 481387e2a8..316567433e 100644
--- a/datasets/ramp Building Footprint Dataset - Dar es Salaam, Tanzania_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json
index 83c946b977..972bf3d9cc 100644
--- a/datasets/ramp Building Footprint Dataset - Dhaka, Bangladesh_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json b/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json
index 479bfdaebb..6eeadb2ed8 100644
--- a/datasets/ramp Building Footprint Dataset - Hpa-an, Myanmar_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json
index 6859e5dbc5..e480bb1684 100644
--- a/datasets/ramp Building Footprint Dataset - Jashore, Bangladesh_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json b/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json
index 8901e64261..e9317023c4 100644
--- a/datasets/ramp Building Footprint Dataset - Karnataka, India_1.json	
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index 1e542261f3..ddf62020a7 100644
--- a/datasets/ramp Building Footprint Dataset - Les Cayes, Haiti_1.json	
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index b9b3b82e4b..9d4b3a67da 100644
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diff --git a/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json b/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json
index e1ee78562e..e2c926ab9d 100644
--- a/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json	
+++ b/datasets/ramp Building Footprint Dataset - Manjama, Sierra Leone_1.json	
@@ -42,7 +42,7 @@
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index 8d80914a0e..e8932c0b02 100644
--- a/datasets/ramp Building Footprint Dataset - Mesopotamia, St. Vincent_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json b/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json
index ace1b191b1..73b50c1431 100644
--- a/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json	
+++ b/datasets/ramp Building Footprint Dataset - Muscat, Oman_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json b/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json
index 2720b0e360..2ec4f24557 100644
--- a/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json	
+++ b/datasets/ramp Building Footprint Dataset - Mzuzu, Malawi_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json b/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json
index 44a45cb0f8..ab1337c966 100644
--- a/datasets/ramp Building Footprint Dataset - N'Djamena, Chad_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json b/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json
index 70d9ef3b73..a8d0741c78 100644
--- a/datasets/ramp Building Footprint Dataset - Nairobi, Kenya_1.json	
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@@ -42,7 +42,7 @@
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diff --git a/datasets/ramp Building Footprint Dataset - Paris, France_1.json b/datasets/ramp Building Footprint Dataset - Paris, France_1.json
index 6ece43b5cc..478cef578b 100644
--- a/datasets/ramp Building Footprint Dataset - Paris, France_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json b/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json
index 69ea8073a0..2ddb88c270 100644
--- a/datasets/ramp Building Footprint Dataset - Shanghai, China_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json b/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json
index 3b864029bf..d48d55073d 100644
--- a/datasets/ramp Building Footprint Dataset - Sylhet, Bangladesh_1.json	
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diff --git a/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json b/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json
index 7ef8fb14bc..1c01436f7e 100644
--- a/datasets/ramp Building Footprint Dataset - Wa, Ghana_1.json	
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diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json
index e333a4a02b..10005bfa7c 100644
--- a/nasa_cmr_catalog.json
+++ b/nasa_cmr_catalog.json
@@ -181,266 +181,6 @@
         "description": "The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-13614/",
         "license": "proprietary"
     },
-    {
-        "id": "10-16904-10_1.0",
-        "title": "DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.92665, 46.71291, 9.92665, 46.71291",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814554-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814554-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/10-16904-10_1.0",
-        "description": "This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.      The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.    Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g.  the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated.  The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-19_1.0",
-        "title": "DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.8523577, 46.7001529, 9.9359287, 46.7393031",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814574-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814574-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/10-16904-19_1.0",
-        "description": "The data presented here corresponds to the publication \"A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation\" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland)  and its influence on snow accumulation. The dataset contains:    * Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.  * ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).  * LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).  * meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.  * TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.    For more details about the simulation and observation data, see Gerber et al., 2017.    __Publication__: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-1_7",
-        "title": "WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.809568, 46.829598, 9.809568, 46.829598",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814541-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814541-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/10-16904-1_7",
-        "description": "Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.     __Publication__  Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-21_1.0",
-        "title": "Wind crust formation: SnowMicroPen data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.86752, 46.80798, 9.86752, 46.80798",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814603-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814603-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/10-16904-21_1.0",
-        "description": "This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.     The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.    Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.    This dataset is the basis of the following publication:    Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-22_1.0",
-        "title": "Wind crust formation: Microsoft Kinect data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.86752, 46.80798, 9.86752, 46.80798",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814617-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814617-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/10-16904-22_1.0",
-        "description": "This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.    The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.    Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.    This dataset is the basis of the following publication:    Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-23_1.0",
-        "title": "Precipitation Scaling Data Set (V\u00f6geli et al., Frontiers)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.7098541, 46.6866604, 10.0037384, 46.8606605",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814633-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814633-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/10-16904-23_1.0",
-        "description": "Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.    This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.     The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.    Please cite:    _V\u00f6geli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._    Dataset is provided as a single zip file.   The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.   The archive also contains the file: \"ReadMeMetadataDataSetPrecipitationScaling\" which explains the data structure.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-2_1",
-        "title": "Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.809568, 46.829598, 9.809568, 46.829598",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814584-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814584-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/10-16904-2_1",
-        "description": "Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N\u00b083, IACS Contribution N\u00b01, UNESCO-IHP, Paris].",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-3_1",
-        "title": "Forest Access Roads 2013",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814652-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814652-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/10-16904-3_1",
-        "description": "In 2013\u20132014, a survey was conducted in Switzerland to update the Forest Access Roads geo-dataset within the framework of the Swiss National Forest Inventory (NFI). The resulting nationwide dataset contains valuable information on truck-accessible forest roads that can be used to transport wood. The survey involved interviewing staff from the approximately 800 local forest services in Switzerland and recording the data first on paper maps and then in digitized form. The data in the NFI on the forest roads could thus be updated and additional information regarding their trafficability for specific categories of truck included. The information has now been attached to the geometries of the Roads and Tracks of the swissTLM3D (release 2012) of the Federal Office of Topography swisstopo. The resulting data are suitable for statistical analyses and modeling, but further (labour-intensive) validation work would be necessary if they are to be used as a basis for applications requiring more spatial accuracy, such as navigation systems. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available for third parties for non-commercial use provided they have purchased a TLM license.     __Related Publication__: [doi: 10.3188/szf.2016.0136](http://dx.doi.org/10.3188/szf.2016.0136)",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-4_1",
-        "title": "TRAMM project Ruedlingen experimental landslide dataset, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2015-01-01",
-        "end_date": "2015-01-01",
-        "bbox": "8.56659, 47.56685, 8.56659, 47.56685",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814663-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814663-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/10-16904-4_1",
-        "description": "A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38\u00b0 to 43\u00b0 with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2).     __Publications__    1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992\u20138004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560).    2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-5_1",
-        "title": "TRAMM project - experimental hydrological and hydrogeological dataset of a landslide prone hillslope. Rufiberg, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2015-01-01",
-        "end_date": "2015-01-01",
-        "bbox": "8.5544251, 47.0889606, 8.5544251, 47.0889606",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814727-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814727-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/10-16904-5_1",
-        "description": "Rufiberg is a pre-alpine meadow site in Switzerland where shallow landslides have been observed after past intense rain storms. In order to assess the triggering mechanisms of these landslides, a comprehensive investigation was conducted within the project TRAMM from Nov 2009 to Oct 2012. It included meteorological observations, soil moisture measurements, bedrock groundwater measurements. The Rufiberg is located at the NW side of the Gnipen to the north of the village Arth-Goldau in the Canton of Schwyz. In the summer months, the site is used for pasturing. Usually, from December to March a snow cover is present at the Rufiberg. The site is at an altitude between 1080 \u2013 1180 m asl, is ENE oriented, and has an average slope of 30 -35\u00b0. The Subalpine Molasse in the region is inclined with 30 - 35\u00b0 to SE. In the area of the field site, beds of conglomerate with several m of thickness alter with beds of sandstone and marlstone. A ca. 2 \u2013 5 m thick eluvium/colluvium layer composed of silty and sandy clay covers the bedrock. This site has been chosen because on one hand, during heavy rainfall events, e.g. autumn 2005, numerous landslides occur in the region of the Gnipen and the Rufiberg. On the other hand, the Rufiberg is very appropriate for experiments due its location away from infrastructures and due to its accessibility. The goal of the investigation was to understand the hydrology and hydrogeology of the slope with regard to shallow landslides.     More information: Br\u00f6nnimann, C., St\u00e4hli, M., Schneider, P., Seward, L. and Springman, S.M. 2013. Bedrock exfiltration as a triggering mechanism for shallow landslides. Water Resources Research, 49 (9): 5155\u20135167. DOI: 10.1002/wrcr.20386.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-6_1",
-        "title": "Wind tunnel measurement data of drifting snow and turbulent wind fluctuations",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.84726, 46.81204, 9.84726, 46.81204",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814796-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814796-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/10-16904-6_1",
-        "description": "The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u\u2019) and the vertical (w\u2019) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.     __Publication__ http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-7_1",
-        "title": "More than one century of hydrological monitoring in two small catchments with different forest coverage, Sperbelgraben and Rappengraben (Switzerland)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "7.84105, 47.01343, 7.8903, 47.03105",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814861-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814861-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/10-16904-7_1",
-        "description": "Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land-use and climate change. Here the longest continuous runoff records (1903 - 2015) from small catchments (less than 1 km2) in Switzerland (and possibly worldwide) are provided as a data set. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized in St\u00e4hli et al., Environ Monit Assess (2011). The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty (also discussed in St\u00e4hli et al., 2011). This treasure trove of data can be used in various ways, eg. for analysis of the generalized extreme value distributions of the two catchments, of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic models.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-8_1",
-        "title": "Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "180, -89.7, -180, -37",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814950-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814950-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/10-16904-8_1",
-        "description": "This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.    _Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._    __Related publication__: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)    Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-9_1",
-        "title": "High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "114, -66, 122, -63",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814985-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814985-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/10-16904-9_1",
-        "description": "This dataset comprises of a post-processed set of terrestrial laser scans (TLS\u2019s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the \u2018xyz\u2019 coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.    ###How to cite:  Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-24_1.0",
-        "title": "Influence of slope-scale snowmelt on catchment response simulated with the Alpine3D model",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.8197174, 46.672292, 9.9893188, 46.8012345",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815030-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815030-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-16904-envidat-24_1.0",
-        "description": "# Abstract  Snow and hydrological modeling in alpine environments remains a challenge because of the complexity of the processes complexity affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high-alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014-2015. Based on recent advances in Alpine3D, we examine how modeled snow distributions, and modeled liquid water transport within the snowpack influence runoff dynamics. By combining these results with multi-scale field data (snow lysimeter data, distributed snow depths and streamflow), we demonstrate the added value of a more realistic representation of snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the snowpack mass balance errors are corrected (R2 = 0.95 vs. R2 = 0.61). At the sub-basin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originated in the shallower areas while an extended melting period (by more than a month) is caused by slower snowmelt from deeper areas. This result is a marked improvement over results obtained using a less heterogeneous snow distribution (i.e., traditional precipitation interpolation method). Catchment hydrological response is also improved by the more realistic representation of snowpack heterogeneity (Nash coefficient of 0.85 vs. 0.74), even though the calibration process smoothens out the differences.   The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at the sub-basin and basin scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well-instrumented area and present a model validation from multi-scale experimental datasets.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-25_1",
-        "title": "DISCHMEX - High-resolution daily snow ablation rates in an Alpine environment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.92665, 46.71291, 9.92665, 46.71291",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814543-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814543-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/10-16904-envidat-25_1",
-        "description": "We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007\u00b0. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-27_1.0",
-        "title": "Calibration data for empirical mortality models of 18 European tree species",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "5.8447266, 45.7521934, 11.7773438, 53.917281",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814553-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814553-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/10-16904-envidat-27_1.0",
-        "description": "The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.     ## Inventory data  Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH \u2265 4 cm for Switzerland and \u2265 7 cm for Germany.     ## Data selection  We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH \u2265 7 cm).  We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..     ## Predictors of tree mortality  We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.     ## Further information  For further information, refer to H\u00fclsmann _et al_. (in press) How to kill a tree \u2013 Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-28_1.0",
-        "title": "Snowfarming data set Davos and Martell 2015",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.868, 46.517, 10.727, 46.808",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814571-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814571-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/10-16904-envidat-28_1.0",
-        "description": "Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.     The data set contains for each site:    * 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)    * 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)    Input files for SNOWPACK model:    * .sno: snow profile at the end of winter    * .smet: meteorological data measured by weather stations in the area    For more details see  Gr\u00fcnewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-29_1.0",
-        "title": "Automatic detection of avalanches",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.78759, 46.80616, 9.78759, 46.80616",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814583-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814583-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/10-16904-envidat-29_1.0",
-        "description": "This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.",
-        "license": "proprietary"
-    },
-    {
-        "id": "10-16904-envidat-30_1.0",
-        "title": "Expedition to Princess Elisabeth Antarctica Station, 2016/2017",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "23.35, -71.95, 23.35, -71.95",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814600-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814600-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/10-16904-envidat-30_1.0",
-        "description": "This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.    Please refer to the README for more information about the data.    This dataset is the basis of the following publication:    Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.",
-        "license": "proprietary"
-    },
     {
         "id": "10.25921/0haq-t221_Not Applicable",
         "title": "Chlorofluorocarbons, nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22 (NCEI Accession 0186104)",
@@ -4601,19 +4341,6 @@
         "description": "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast \"live\" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2016/17 season.  Purpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section).",
         "license": "proprietary"
     },
-    {
-        "id": "2016gl071822_1.0",
-        "title": "Energy- and momentum-conserving model of splash entrainment in sand and snow saltation",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "6.56689, 46.51959, 6.56689, 46.51959",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814614-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814614-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/2016gl071822_1.0",
-        "description": "The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).    ## Manuscript Abstract:  Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.",
-        "license": "proprietary"
-    },
     {
         "id": "201718010_1",
         "title": "Aurora Australis Voyage 1 2017/18 Track and Underway Data",
@@ -7032,19 +6759,6 @@
         "description": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and   functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\u00e2\u0080\u00a2\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\u00e2\u0080\u00a2\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\u00e2\u0080\u00a2\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \u00e2\u0080\u00a2\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \u00e2\u0080\u00a2\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.",
         "license": "proprietary"
     },
-    {
-        "id": "3d_snow_models_4.0",
-        "title": "3D_Snow_Models",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8471832, 46.8146287, 9.8471832, 46.8146287",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/3d_snow_models_4.0",
-        "description": "The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).",
-        "license": "proprietary"
-    },
     {
         "id": "3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA",
         "title": "METOP GOME-2 - Formaldehyde (HCHO) - Global",
@@ -14269,7 +13983,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877465-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877465-POCLOUD.html",
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         "description": "GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018.   NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019).   The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image.   In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0).  Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel.   The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, where gridded L2P SSTs are reported, and BT layers omitted.",
         "license": "proprietary"
     },
@@ -14282,7 +13996,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877612-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877612-POCLOUD.html",
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         "description": "The ACSPO G16/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G16/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L2P-v2.70. The L3C output files are 1hr granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules per 24hr interval, with a total data volume of 0.2GB/day. Fill values are reported at all invalid pixels, including pixels with 5 km inland.  For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). All valid SSTs in L3C are recommended for users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877626-POCLOUD.html",
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         "description": "GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.",
         "license": "proprietary"
     },
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         "description": "The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
         "license": "proprietary"
     },
@@ -19163,27 +18877,27 @@
     },
     {
         "id": "AERIALDIGI_Not provided",
-        "title": "Aircraft Scanners",
-        "catalog": "USGS_LTA STAC Catalog",
+        "title": "Aircraft Scanners - AERIALDIGI",
+        "catalog": "CEOS_EXTRA STAC Catalog",
         "state_date": "1987-10-06",
         "end_date": "",
         "bbox": "-180, 24, -60, 72",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI_Not%20provided",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections?cursor=eyJqc29uIjoiW1wiYWZyaWNhbiBtYXJpbmUgYXRsYXNcIixcIkNFT1NfRVhUUkFcIixcIkFmcmljYW5fTWFyaW5lX0F0bGFzXCIsXCJub3QgcHJvdmlkZWRcIiwyMjMyNDU5MzE2LDFdIiwidW1tIjoiW1wiYWZyaWNhbiBtYXJpbmUgYXRsYXNcIixcIkNFT1NfRVhUUkFcIixcIkFmcmljYW5fTWFyaW5lX0F0bGFzXCIsXCJub3QgcHJvdmlkZWRcIiwyMjMyNDU5MzE2LDFdIn0%3D/AERIALDIGI_Not%20provided",
         "description": "The National Aeronautics and Space Administration (NASA) Aircraft        Scanners data set contains digital imagery acquired from several        multispectral scanners, including Daedalus thematic mapper simulator       scanners and  the thermal infrared multispectral scanner.  Data are       collected from  selected areas over the conterminous United States,       Alaska, and Hawaii by  NASA ER-2 and NASA C-130B aircraft, operating       from the NASA Ames Research  Center in Moffett Field, California, and by       NASA Learjet aircraft, operating  from Stennis Space Center in Bay St.       Louis, Mississippi.  Limited  international acquisitions also are       available.              In cooperation with the Jet Propulsion Laboratory and Daedalus        Enterprises,Inc., NASA developed several multispectral sensors.  The       data  acquired from these sensors supports NASA's Airborne Science and       Applications  Program and have been identified as precursors to the       instruments scheduled to  fly on Earth Observing System platforms.              THEMATIC MAPPER SIMULATOR              The Thematic Mapper Simulator (TMS) sensor is a line scanning device          designed for a variety of Earth science applications.  Flown aboard              NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field  of              View of 1.25 milliradians with a ground resolution of 81 feet (25  meters)              at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per  second              with 716 pixels per scan line.  Swath width is 8.3 nautical miles              (15.4 kilometers) at 65,000 feet while the scanner's Field of View  is              42.5 degrees.                NS-001 MULTISPECTRAL SCANNER               The NS-001multispectral scanner is a line scanning device designed  to              simulate Landsat thematic mapper (TM) sensor performance, including  a              near infrared/short-wave infrared band used in applications similar  to those         of the TM sensor (e.g., Earth resources mapping, vegetation/land  cover          mapping, geologic studies).  Flown aboard NASA C-130B aircraft, the  NS-001       sensor has a nominal Instantaneous Field of View of 2.5 milliradians     with a       ground resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a       variable scan rate (10 to 100 scans per second) with 699  pixels per scan line,       but the available motor drive supply restricts the  maximum stable scan speed       to approximately 85 revolutions per second. A scan rate  of 100 revolutions per       second is possible, but not probable, for short  scan lines; therefore, a       combination of factors, including aircraft  flight requirements and maximum       scan speed, prevent scanner operation below 1,500 feet.  Swath width is 3.9       nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or       field of regard for the sensor is 100 degrees, plus or minus 15 degrees for       roll compensation.       THERMAL INFRARED MULTISPECTRAL SCANNER       The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line  scanning         device originally designed for geologic applications.  Flown aboard NASA       C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor  has a              nominal Instantaneous Field of View of 2.5 milliradians with a  ground              resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a              selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698  pixels          per scan line.  Swath width is 2.6 nautical miles (4.8 kilometers)  at            10,000 feet while the scanner's Field of View is 76.56 degrees.",
         "license": "proprietary"
     },
     {
         "id": "AERIALDIGI_Not provided",
-        "title": "Aircraft Scanners - AERIALDIGI",
-        "catalog": "CEOS_EXTRA STAC Catalog",
+        "title": "Aircraft Scanners",
+        "catalog": "USGS_LTA STAC Catalog",
         "state_date": "1987-10-06",
         "end_date": "",
         "bbox": "-180, 24, -60, 72",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI_Not%20provided",
         "description": "The National Aeronautics and Space Administration (NASA) Aircraft        Scanners data set contains digital imagery acquired from several        multispectral scanners, including Daedalus thematic mapper simulator       scanners and  the thermal infrared multispectral scanner.  Data are       collected from  selected areas over the conterminous United States,       Alaska, and Hawaii by  NASA ER-2 and NASA C-130B aircraft, operating       from the NASA Ames Research  Center in Moffett Field, California, and by       NASA Learjet aircraft, operating  from Stennis Space Center in Bay St.       Louis, Mississippi.  Limited  international acquisitions also are       available.              In cooperation with the Jet Propulsion Laboratory and Daedalus        Enterprises,Inc., NASA developed several multispectral sensors.  The       data  acquired from these sensors supports NASA's Airborne Science and       Applications  Program and have been identified as precursors to the       instruments scheduled to  fly on Earth Observing System platforms.              THEMATIC MAPPER SIMULATOR              The Thematic Mapper Simulator (TMS) sensor is a line scanning device          designed for a variety of Earth science applications.  Flown aboard              NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field  of              View of 1.25 milliradians with a ground resolution of 81 feet (25  meters)              at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per  second              with 716 pixels per scan line.  Swath width is 8.3 nautical miles              (15.4 kilometers) at 65,000 feet while the scanner's Field of View  is              42.5 degrees.                NS-001 MULTISPECTRAL SCANNER               The NS-001multispectral scanner is a line scanning device designed  to              simulate Landsat thematic mapper (TM) sensor performance, including  a              near infrared/short-wave infrared band used in applications similar  to those         of the TM sensor (e.g., Earth resources mapping, vegetation/land  cover          mapping, geologic studies).  Flown aboard NASA C-130B aircraft, the  NS-001       sensor has a nominal Instantaneous Field of View of 2.5 milliradians     with a       ground resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a       variable scan rate (10 to 100 scans per second) with 699  pixels per scan line,       but the available motor drive supply restricts the  maximum stable scan speed       to approximately 85 revolutions per second. A scan rate  of 100 revolutions per       second is possible, but not probable, for short  scan lines; therefore, a       combination of factors, including aircraft  flight requirements and maximum       scan speed, prevent scanner operation below 1,500 feet.  Swath width is 3.9       nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or       field of regard for the sensor is 100 degrees, plus or minus 15 degrees for       roll compensation.       THERMAL INFRARED MULTISPECTRAL SCANNER       The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line  scanning         device originally designed for geologic applications.  Flown aboard NASA       C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor  has a              nominal Instantaneous Field of View of 2.5 milliradians with a  ground              resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a              selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698  pixels          per scan line.  Swath width is 2.6 nautical miles (4.8 kilometers)  at            10,000 feet while the scanner's Field of View is 76.56 degrees.",
         "license": "proprietary"
     },
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         "description": "Himawari-8 (H08) was launched on 7 October 2014 into its nominal position at 140.7-deg E, and declared operational on 7 July 2015. The Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, and 12.3 um) are suitable for SST. Accurate sensor calibration, image navigation and (co)registration, high spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping) offer vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P and MTSAT-2 Imagers. From altitude 35,800km, H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km at view zenith angle 67-deg, with a 10-min temporal sampling. The AHI L2P (swath) SST product is derived at the native sensor resolution using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO processes every 10-min FD data, identifies good quality ocean pixels (Petrenko et al., 2010) and derives SST using the four-band (8.4, 10.3, 11.2 and 12.3um) Non-Linear SST (NLSST) regression algorithm (Petrenko et al., 2014), trained against in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise. The collated L2P reports SSTs and brightness temperatures (BTs) in clear-sky water pixels (defined as ocean, sea, lake or river), and fill values elsewhere. All pixels with valid SSTs are recommended for use. ACSPO files also include sun-sensor geometry, l2p_flags (day/night, land, ice, twilight, and glint flags), and NCEP wind speed. The L2P is reported in NetCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume 0.6GB/day. Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). The H08 AHI SSTs and BTs are continuously validated against in situ data in SQUAM (Dash et al, 2010), and RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), 0.02-deg equal-angle gridded ACSPO L3C product is available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L3C-v2.70.",
         "license": "proprietary"
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         "description": "The ACSPO H08/AHI L3C (Level 3 Collated) product is a gridded version of the ACSPO H08/AHI L2P product available at https://podaac.jpl.nasa.gov/dataset/AHI_H08-STAR-L2P-v2.70. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 24 granules available per 24hr interval, with a total data volume of 0.2GB/day. Valid SSTs are found over clear-sky oceans, sea, lakes or rivers, with fill values reported elsewhere. The following layers are reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST (Petrenko et al., 2016). The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2251465126-POCLOUD.html",
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         "description": "These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL).  SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013. The major difference between these data and the Operational Geophysical Data Record (OGDR) data produced by the project is that the orbit from SARAL has been adjusted using SSHA differences with those from the OSTM/Jason-2 GPS-OGDR-SSHA product at inter-satellite crossover locations. This produces a more accurate NRT orbit altitude for SARAL with accuracy of 1.5 cm (RMS), taking advantage of the 1 cm (radial RMS) accuracy of the GPS-based orbit used for the OSTM/Jason-2 GPS-OGDR-SSHA product. This dataset also contains all data from the project (reduced) OGDR, and improved altimeter wind speeds and sea state bias correction. More information on the SARAL mission can be found at: http://www.aviso.oceanobs.com/en/missions/current-missions/saral.html",
         "license": "proprietary"
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         "description": "The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude.  This annually updated data product extends from October 14, 1992 through November 4, 2009.  SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast.  Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region.  Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.",
         "license": "proprietary"
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         "description": "The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude.  This annually updated data product extends from October 14, 1992 through January 19, 2011.  SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast.  Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region.  Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756.",
         "license": "proprietary"
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         "description": "This product provides a \u201cFinal\u201d (Refined) Level-2 Sea Surface Temperature (SST) (currently identified by \"v8.2\" within the file name) for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, which is derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by Remote Sensing Systems (RSS, or REMSS). AMSR2 was launched on 18 May 2012, onboard the Global Change Observation Mission - Water (GCOM-W) satellite developed by the Japan Aerospace Exploration Agency (JAXA). The GCOM-W mission aims to establish the global and long-term observation system to collect data, which is needed to understand mechanisms of climate and water cycle variations, and demonstrate its utilization. AMSR2 onboard the first generation of the GCOM-W satellite will continue Aqua/AMSR-E observations of water vapor, cloud liquid water, precipitation, SST, sea surface wind speed, sea ice concentration, snow depth, and soil moisture. AMSR2 is a remote sensing instrument for measuring weak microwave emission from the surface and the atmosphere of the Earth. The antenna of AMSR2 rotates once per 1.5 seconds and obtains data over a 1450 km swath. This conical scan mechanism enables AMSR2 to acquire a set of daytime and nighttime data with more than 99% coverage of the Earth every 2 days. The \u201cFinal\u201d SSTs are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The v8.2 supersedes the previous v8a dataset which can be found at https://www.doi.org/10.5067/GHAM2-2PR8A. ",
         "license": "proprietary"
     },
@@ -21965,7 +21679,7 @@
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS)  is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
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         "description": "The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS)  is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"_rt_\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7\" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537006834-POCLOUD.html",
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         "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL061Mv03 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability are provided as an ASCII table.",
         "license": "proprietary"
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         "description": "The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution,  7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument.  Bias fields are constructed by differencing in situ from  Aquarius derived SSS fields obtained separately using ascending and descending satellite observations  for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional  Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes  are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.",
         "license": "proprietary"
     },
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         "description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD7.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
         "license": "proprietary"
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         "description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-A at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD7.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-A platform. For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
         "license": "proprietary"
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         "description": "This dataset contains model output interpolated in space and time to the ESDR product from the MetOp-A ASCAT (ASCAT-A) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-A Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.  <br><br>  The dataset represents the first science quality release of these data with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.  <br><br>  The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "description": "This dataset represents the first historically reprocessed Level 2 ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 25 km grid.  Products at 25-km sampling are less noisy than 12.5-km products, but also contain less geophysical information on small scales and near the coasts. The wind vector retrievals are currently processed using the CMOD7 geophysical model function using a Hamming filter to spatially average the Level 1 Sigma-0 data over 25 km swath grid cells. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 25-km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-25km. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.",
         "license": "proprietary"
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         "description": "This dataset represents the first historically reprocessed Level 2 coastal ocean surface wind vector climate data record from the Advanced Scatterometer (ASCAT) on MetOp-A sampled on a 12.5 km grid. This coastal dataset utilizes a spatial box filter to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset and obtains additional winds near the coast. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the vector cell wind using the same CMOD7 geophysical model function as in the operational OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds are computed as close to ~15 km from the coast. Each file corresponds to one complete orbit and is provided in netCDF version 3 format. The beginning of the orbit files is defined near the South Pole. ASCAT is a C-band fan beam radar scatterometer, providing two independent swaths of backscatter retrievals, aboard the MetOp-A platform in sun-synchronous polar orbit. It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). For more information on the MetOp mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For access to more contemporaneous and near-real-time MetOp-A ASCAT 12.5km data, please visit: https://podaac.jpl.nasa.gov/dataset/ASCATA-L2-Coastal. For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used. Use cases and feedback on the products will be much appreciated and in fact helps to sustain the reprocessing capability.",
         "license": "proprietary"
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         "description": "This dataset contains operational near-real-time Level 2 ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 25 km sampling resolution (note: the effective resolution is 50 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). The wind vector retrievals are currently processed using the CMOD.n geophysical model function using a Hamming filter to spatially average the Sigma-0 data in the ASCAT L1B data. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
         "license": "proprietary"
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         "description": "This dataset contains operational near-real-time Level 2 coastal ocean surface wind vector retrievals from the Advanced Scatterometer (ASCAT) on MetOp-B at 12.5 km sampling resolution (note: the effective resolution is 25 km). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). This coastal dataset differs from the standard 12.5 and 25 km datasets in that it utilizes a spatial box filter (rather than the Hamming filter) to generate a spatial average of the Sigma-0 retrievals from the Level 1B dataset; all full resolution Sigma-0 retrievals within a 15 km radius of the wind vector cell centroid are used in the averaging. Since the full resolution L1B Sigma-0 retrievals are used, all non-sea retrievals are discarded prior to the Sigma-0 averaging. Each box average Sigma-0 is then used to compute the wind vector cell using the same CMOD5.n geophysical model function as in the standard OSI SAF ASCAT wind vector datasets. With this enhanced coastal retrieval, winds can be computed as close to ~15 km from the coast, as compared to the static ~35 km land mask in the standard 12.5 km dataset. Each file is provided in netCDF version 3 format, and contains one full orbit derived from 3-minute orbit granules. Latency is approximately 2 hours from the latest measurement. The beginning of the orbit is defined by the first wind vector cell measurement within the first 3-minute orbit granule that starts north of the Equator in the ascending node. ASCAT is a C-band dual swath fan beam radar scatterometer providing two independent swaths of backscatter retrievals in sun-synchronous polar orbit aboard the MetOp-B platform. For more information on the MetOp-B mission, please visit: https://www.eumetsat.int/our-satellites/metop-series . For more timely announcements, users are encouraged to register with the KNMI scatterometer email list: scat@knmi.nl. Users are also highly advised to check the dataset user guide periodically for updates and new information on known problems and issues. All intellectual property rights of the OSI SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words \"copyright (year) EUMETSAT\" on each of the products used.",
         "license": "proprietary"
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         "description": "This dataset contains model output interpolated in space and time to observations from the MetOp-B ASCAT (ASCAT-B) instrument (a satellite-based scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. These auxiliary fields are included to complement the scatterometer observations. Model variables include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. The modeled fields are provided on a non-uniform grid within the sampled locations of the ASCAT-B Level 2 product, and at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.  <br><br>  The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) cleaned up ancillary data points in between the left/right swaths for improved collocation with available satellite data, 2) improved variable metadata, 3) removed the GlobCurrent stokes drift variables, and 4) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this Version 1.1 release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit.  <br><br>  The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "license": "proprietary"
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         "title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.html",
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         "id": "ATL04_006",
         "title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006",
-        "catalog": "NSIDC_CPRD STAC Catalog",
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-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html",
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         "title": "ATLAS/ICESat-2 L3A Land and Vegetation Height V006",
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-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL08_006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.html",
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     {
         "id": "ATL08_006",
         "title": "ATLAS/ICESat-2 L3A Land and Vegetation Height V006",
-        "catalog": "NSIDC_ECS STAC Catalog",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.html",
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         "license": "proprietary"
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@@ -31696,26 +31410,26 @@
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         "id": "ATL09_006",
         "title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006",
-        "catalog": "NSIDC_ECS STAC Catalog",
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         "state_date": "2018-10-13",
         "end_date": "",
         "bbox": "-180, -90, 180, 90",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006",
         "description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
         "license": "proprietary"
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         "id": "ATL09_006",
         "title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006",
-        "catalog": "NSIDC_CPRD STAC Catalog",
+        "catalog": "NSIDC_ECS STAC Catalog",
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-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.html",
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         "license": "proprietary"
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@@ -31735,26 +31449,26 @@
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         "id": "ATL10_006",
         "title": "ATLAS/ICESat-2 L3A Sea Ice Freeboard V006",
-        "catalog": "NSIDC_ECS STAC Catalog",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.html",
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         "license": "proprietary"
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     {
         "id": "ATL10_006",
         "title": "ATLAS/ICESat-2 L3A Sea Ice Freeboard V006",
-        "catalog": "NSIDC_CPRD STAC Catalog",
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-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL10_006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.html",
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         "license": "proprietary"
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@@ -31787,26 +31501,26 @@
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         "id": "ATL12_006",
         "title": "ATLAS/ICESat-2 L3A Ocean Surface Height V006",
-        "catalog": "NSIDC_CPRD STAC Catalog",
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-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL12_006",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.html",
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         "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.",
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         "id": "ATL20_004",
         "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004",
-        "catalog": "NSIDC_ECS STAC Catalog",
+        "catalog": "NSIDC_CPRD STAC Catalog",
         "state_date": "2018-10-14",
         "end_date": "",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL20_004",
         "description": "ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.",
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         "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003",
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         "title": "ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003",
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         "description": "ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.",
         "license": "proprietary"
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     {
         "id": "ATL22_003",
         "title": "ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003",
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         "description": "ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.",
         "license": "proprietary"
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     {
         "id": "ATL22_003",
         "title": "ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003",
-        "catalog": "NSIDC_CPRD STAC Catalog",
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         "license": "proprietary"
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@@ -32144,7 +31858,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197627-POCLOUD.html",
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         "description": "Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz.",
         "license": "proprietary"
     },
@@ -33327,7 +33041,7 @@
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-18 platform (launched 20 May 2005) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform),  acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC).  HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir.  Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.",
         "license": "proprietary"
     },
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform),  acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC).  HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. This particular dataset is produced from GAC data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir.  Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.",
         "license": "proprietary"
     },
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         "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009) produced and used operationally in  oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform),  acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. AVHRR data are acquired in three formats: High Resolution Picture Transmission (HRPT), Local Area Coverage (LAC), and Global Area Coverage (GAC).  HRPT data are full resolution image data transmitted to a ground stations as they are collected. LAC are also full resolution data, but the acquisition is prescheduled and recorded with an on-board tape recorder for subsequent transmission during a station overpass. GAC data provide daily subsampled global coverage recorded on tape recorders and then transmitted to a ground station. This particular dataset is derived from LAC data. Further binning and averaging of the 1.1 km  LAC pixels results in a final dataset resolution of 2.2 km. The coverage of the LAC data can vary but generally contains scenes over the oceans adjacent to Australia and the North Indian Ocean.",
         "license": "proprietary"
     },
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         "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. MetOp-A launched on 19 October 2006 is the first in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN.  A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTA-3US28",
         "license": "proprietary"
     },
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         "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite A (Metop-A) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MA-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-A AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MA-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).",
         "license": "proprietary"
     },
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         "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-B launched on 17 September 2012 is the second in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN.  A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTB-3US28",
         "license": "proprietary"
     },
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         "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite B (Metop-B) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MB-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-B AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MB-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).",
         "license": "proprietary"
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         "description": "The MetOp First Generation (FG) is a European multi-satellite program jointly established by ESA and EUMETSAT, comprising three satellites, MetOp-A, -B and -C. The primary sensor onboard MetOp-FG, the Advanced Very High Resolution Radiometer/3 (AVHRR/3) contributed by NOAA, measures Earth emissions and reflectances in 5 out of 6 available bands (centered at 0.63, 0.83, 1.61, 3.7, 11 and 12 microns), in a swath of 2,600km from an 817km altitude. These data are collected in a Full Resolution Area Coverage (FRAC) mode, with pixel size of 1.1km at nadir. Metop-C launched on 7 November 2018 is the third and last in the MetOp-FG series. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) Level 2 Preprocessed (L2P) SST product is derived at the full AVHRR FRAC resolution and reported in 10 minute granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Subskin SSTs are derived using the regression Nonlinear SST (NLSST) algorithm, which employs three bands (3.7, 11 and 12 microns) at night and two bands (11 and 12 microns) during the day. The ACSPO AVHRR FRAC L2P product is monitored and validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014, https://doi.org/10.1175/JTECH-D-13-00121.1 ), in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010, https://doi.org/10.1175/2010JTECHO756.1 ). SST imagery and clear-sky masking are continuously evaluated, and checked for consistency with other sensors and platforms, in the ACSPO Regional Monitor for SST (ARMS) system. MetOp-A orbital characteristics and AVHRR/3 sensor performance are tracked in the NOAA 3S system (He et al., 2016, https://doi.org/10.3390/rs8040346 ).The L2P Near Real Time (NRT) SST files are archived at PO.DAAC with 3-6 hours latency, and then replaced by the Re-ANalysis (RAN) SST after about 2 months later with identical file names. Two features can be used to identify them: different file name time stamps and netCDF global attribute metadata source=NOAA-NCEP-GFS for NRT and source=MERRA-2 for RAN.  A reduced size (0.45GB/day), equal-angle gridded (0.02-deg resolution) ACSPO L3U product is available at https://doi.org/10.5067/GHMTC-3US28",
         "license": "proprietary"
     },
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         "description": "This L3U (Level 3 Uncollated) dataset contains global daily Sea Surface Temperature (SST) on a 0.02 degree grid resolution. It is produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear Sky Processor for Ocean (ACSPO) using L2P (Level 2 Preprocessed) product acquired from the Meteorological Operational satellite C (Metop-C) Advanced Very High Resolution Radiometer 3 (AVHRR/3) (https://podaac.jpl.nasa.gov/dataset/AVHRRF_MC-STAR-L2P-v2.80 ) in Full Resolution Area Coverage (FRAC) mode as input. It is distributed as 10-minute granules in netCDF-4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24-hour interval. Fill values are reported in all invalid pixels, including land pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river), and up to 5 km inland, the following major layers are reported: SSTs and ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags). Only input L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. Ancillary layers include wind speed and ACSPO minus reference Canadian Meteorological Centre (CMC) Level 4 (L4) SST. The ACSPO Metop-C AVHRR FRAC L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in the NOAA SST Quality Monitor (SQUAM) system (Dash et al, 2010). SST imagery and clear-sky mask are evaluated, and checked for consistency with L2P and other satellites/sensors SST products, in the NOAA ACSPO Regional Monitor for SST (ARMS) system. More information about the dataset is found at AVHRRF_MC-STAR-L2P-v2.80 and in (Pryamitsyn et al., 2021).",
         "license": "proprietary"
     },
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A  (MetOp-A; launched 19 Oct 2006) ) satellite produced and used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European undertaking providing weather data services for monitoring climate and improving weather forecasts.  It was jointly established by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) with a contribution by the US National Oceanic and Atmospheric Administration (NOAA) of an AVHRR sensor identical to those flying on the family of Polar Orbiting Environmental Satellites (POES). AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform),  acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. The MetOp-A platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir.  Further binning and averaging of these pixels results in a final dataset resolution of 8.8 km.",
         "license": "proprietary"
     },
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P data set containing multi-channel Sea Surface Temperature (SST) retrievals derived in real-time from the Advanced Very High Resolution Radiometer (AVHRR) level-1B data from the Meteorological Operational-A (MetOp-A) satellite. The SST data in this data set are used operationally in oceanographic analyses and forecasts by the US Naval Oceanographic Office (NAVO). The MetOp satellite program is a European multi-satellite  program to provide weather data services for monitoring climate and improving weather forecasts. MetOp-A,  MetOp-B and Metop-C were respectively launched on 19 Oct 2006,  17 September 2012 and 7 November 2018. The program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with the US National Oceanic and Atmospheric Administration (NOAA) contributing the AVHRR sensor. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micron) and near-infrared (0.9 micron) regions, the third one is located around 4 (3.6) micron, and the last two sample the emitted thermal radiation, around 11 and 12 micron, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micron. Typically, the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel.  The swath of the AVHRR sensor is a relatively large 2400 km. All MetOp platforms are sun synchronous and generally view the same earth location twice a day (latitude dependent). The ground native resolution of the AVHRR instruments is approximately 1.1 km at nadir and degrades off nadir. This particular data set is produced from legacy Global Area Coverage (GAC) data that are derived from a sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km spatial resolution at nadir. The v2.0 is the updated version from current v1.0 with extensive algorithm improvements and upgrades. The major improvements include: 1) Significant changes in contaminant/cloud detection; 2) Increased the spatial resolution from 9 km to 4 km; 3) Updated compliance with GDS2, ACDD 1.3, and CF 1.6; and 4) Removed the dependency on the High-resolution Infrared Radiation Sounder (HIRS) sensor (only available to MetOp-A/B), thus allowing for the consistent inter-calibration and the processing of MetOp-A/B/C data",
         "license": "proprietary"
     },
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature dataset is produced daily on a 0.25 degree grid at the NOAA National Centers for Environmental Information. This product uses optimal interpolation (OI) by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships, buoys, and Argo floats above 5m depth), and the specific datasets employed may change over time. In the regions with sea-ice concentration higher than 30%, freezing points of seawater are used to generate proxy SSTs.  A preliminary version of this dataset is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. The v2.1 (Huang et al. 2021) is updated from the previous AVHRR_OI-NCEI-L4-GLOB-v2.0 data. Major improvements include: 1) In-situ ship and buoy data changed from the NCEP Traditional Alphanumeric Codes (TAC) to the NCEI merged TAC + Binary Universal Form for the Representation (BUFR) data, with large increases of buoy data included to correct satellite SST biases; 2) Addition of Argo float observed SST data as well, for further correction of satellite SST biases; 3) Satellite input from the METOP-A and NOAA-19 to METOP-A and METOP-B, removing degraded satellite data; 4) Revised ship-buoy SST corrections for improved accuracy; and 5) Revised sea-ice-concentration to SST conversion to remove warm biases in the Arctic region. These updates only apply to data after January 1st, 2016. The data pre 2016 are still the same as v2.0 except for metadata upgrades. NCEI has panned to update the entire dataset from 1982 to fix the In-Situ data ingest and bias correction which exist prior 2016. ",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012).   The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR and SNPP/VIIRS. Global AVHRR level 1b data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. NAR SNPP/VIIRS level 0 data are acquired through direct readout and converted into l1b at CMS. SST is retrieved from the AVHRR and VIIRS infrared channels using a multispectral algorithm. This product is delivered as four six hourly collated files per day on a regular 2km grid. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the North Atlantic Region (NAR) from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-19 platform (launched 6 Feb 2009). This particular dataset is produced by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) in France. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having a operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive sea surface temperature (SST) sometimes in combination with the 3.5 micron channel. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. The NOAA-19 platform is sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The NAR products are SST fields derived from 1km  AVHRR data that are re-mapped onto a 0.02 degree equal angle grid. In the processing chain, global AVHRR level 1b data are acquired at Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. A cloud mask is applied and SST is retrieved from the AVHRR infrared (IR) channels by using  a multispectral technique.  This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
     },
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         "description": "This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans. <br>  The harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys.  The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions. <br>  This dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly coefficients at 0.05 degree (~5 kilometer) resolution (CAM5K30CF). The CAMEL Principal Components Analysis (PCA) input coefficients utilized in the CAMEL high spectral resolution (HSR) algorithm are provided in the CAM5K30CF data product and are congruent to the temporally equivalent CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) emissivity data product. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf).    Provided in the CAM5K30CF product are layers for PCA coefficients, number of PCA coefficients, laboratory version, snow fraction derived from MODIS Snow Cover data (MOD10), latitude, longitude, and the CAMEL quality information. PCA coefficients are dependent on the version of lab Principal Component (PC) data and the number of PCs used.  ",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Infrared Emissivity dataset (UWIREMIS) and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4  (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics.       The integration steps include: adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) 5 kilometer resolution, merging of the 5 ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/4/cam5k30_User_Guide_ATBD_V1.pdf).    Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and Best Fit Emissivity (BFE) quality information.    ",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UW Baseline Fit (UWBF) Emissivity quality information.  ",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined ASTER and MODIS Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty, comprising 3 independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/4/cam5k30_User_Guide_ATBD_V1.pdf). Corresponding emissivity values can be found in the CAM5K30EM data product.    Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.  ",
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity uncertainty at 0.05 degree (~5 kilometer) resolution (CAM5K30UC). CAM5K30UC is an estimation of total emissivity uncertainty comprising three independent components of variability: temporal, spatial, and algorithm. Each measure of uncertainty is provided for all 13 hinge points of emissivity and each latitude-longitude point. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1612/CAMEL_V3_UG_ATBD.pdf). Corresponding emissivity values can be found in the CAM5K30EM (https://doi.org/10.5067/MEaSUREs/LSTE/CAM5K30EM.003) data product.    Provided in the CAM5K30UC product are layers for algorithm uncertainty, spatial uncertainty, temporal uncertainty, total uncertainty, latitude, longitude, spectral wavelength, CAMEL quality, and total uncertainty quality information.  ",
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         "license": "proprietary"
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         "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30-second sampling, daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS GNSS observation files (compact) contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.",
         "license": "proprietary"
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         "description": "This product contains a time series of Earth orientation parameters from the IERS Bulletin A for the Galileo constellation of satellites. The product is generated at JPL's Global Differential GPS Operations Centers.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset consists of differential code biases (DCBs) from a network of ground-based Global Navigation Satellite System (GNSS) station and available from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. DCBs are the systematic errors, or biases, between two GNSS code observations at the same or different frequencies. DCBs are required for code-based positioning of GNSS receivers, extracting ionosphere total electron content (TEC), and other applications. Proper knowledge of DCBs is crucial to many navigation applications but also non-navigation applications such as ionospheric analysis and time transfer. With all of the new signals offered by modernized and new GNSS constellations, analysts now require a comprehensive multi-GNSS DCB product. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_differential_code_bias_product.html.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) Compact Observation Data (30-second sampling, daily, 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure.  The daily GNSS observation files (compact) contain one day of GPS or multi-GNSS observation (30-second sampling) data in compact RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.",
         "license": "proprietary"
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@@ -45885,7 +45599,7 @@
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset consists of ground-based Global Navigation Satellite System (GNSS) GPS Broadcast Ephemeris Data (hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GPS broadcast ephemeris files contain one day of GPS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.",
         "license": "proprietary"
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@@ -46301,7 +46015,7 @@
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         "license": "proprietary"
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         "description": "Global Navigation Satellite System (GNSS) real-time 1 to multi-second sampled data available from the Crustal Dynamics Data Information System (CDDIS). Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS real-time data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Other GNSS (Europe\u2019s Galileo, China\u2019s Beidou, Japan\u2019s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs) are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure; CDDIS began streaming real-time data from these systems in 2015. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (normal points, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The daily SLR normal point observation files contain one hour of SLR data received in the previous one hour period from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Normal_point_data.html.",
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         "description": "This dataset consists of ground-based Satellite Laser Ranging observation data (full-rate, monthly files) from the NASA Crustal Dynamics Data Information System (CDDIS). SLR provides unambiguous range measurements to mm precision that can be aggregated over the global network to provide very accurate satellite orbits, time histories of station position and motion, and many other geophysical parameters. SLR operates in the optical region and is the only space geodetic technique that measures unambiguous range directly. Analysis of SLR data contributes to the terrestrial reference frame, modeling of the spatial and temporal variations of the Earth's gravitational field, and monitoring of millimeter-level variations in the location of the center of mass of the total Earth system (solid Earth-atmosphere-oceans). In addition, SLR provides precise orbit determination for spaceborne radar altimeter missions. It provides a means for sub-nanosecond global time transfer, and a basis for special tests of the Theory of General Relativity. Analysis Centers (ACs) of the International Laser Ranging Service (ILRS) retrieve SLR data on regular schedules to produce precise station positions and velocities for stations in the ILRS network. The monthly SLR full-rate observation files contain data received in the month from a global network of stations ranging to satellites equipped with retroreflectors. Data are available in ILRS data format (older data sets) and/or the Consolidated Ranging Data (CRD) format. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/SLR/Full-rate_data.html.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197622-POCLOUD.html",
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         "description": "Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model.",
         "license": "proprietary"
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         "bbox": "-180, -58, 180, 85",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001791-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001791-SEDAC.html",
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         "description": "Global Volcano Hazard Frequency and Distribution is a 2.5 minute gridded data set based upon the National Geophysical Data Center (NGDC) Volcano Database spanning the period  of 79 through 2000. This database includes nearly 4,000 volcanic events categorized as moderate or above (values 2 through 8) according to the Volcano Explosivity Index (VEI). Most volcanoes are georeferenced to the nearest tenth or hundredth of a degree with a few to the nearest thousandth of a degree. To produce the final output, the frequency of a volcanic hazard is computed for each grid cell, with the data set consequently being classified into deciles (10 classes of approximately equal number of grid cells). The higher the grid cell value in the final output, the higher the relative frequency of hazard posed by volcanoes. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).",
         "license": "proprietary"
     },
@@ -50851,7 +50565,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001787-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001787-SEDAC.html",
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         "description": "Global Volcano Mortality Risks and Distribution is a 2.5 minute grid representing global volcano mortality risks. The data set was constructed using historical hazard-specific mortality loss data from the Emergency Events Database (EM-DAT) maintained by the Centre for Research on the Epidemiology of Disasters (CRED), subnational year 2000 population estimates from Gridded Population of the World, Version 3 (GPWv3), and volcano hazard data from the Global Volcano Hazard Frequency and Distribution data set. Estimates were made as to the mortality numbers associated with volcano hazard. In turn, these mortality estimates were classified into deciles, 10 class of an approximately equal number of grid cells of increasing mortality risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001794-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001794-SEDAC.html",
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         "description": "Global Volcano Proportional Economic Loss Risk Deciles is a 2.5 minute grid of volcano hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
         "license": "proprietary"
     },
@@ -50877,7 +50591,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001793-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001793-SEDAC.html",
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         "description": "Global Volcano Total Economic Loss Risk Deciles is a 2.5 minute grid of global volcano total economic loss risks. First, subnational distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where applicable, the proportional contribution of subnational Units are determined following the methodology of Sachs et al. (2003) and these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational Unit. Once a national GDP has been spatially stratified into the smallest administrative Units available, it is further distributed based upon Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each Unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
         "license": "proprietary"
     },
@@ -51896,7 +51610,7 @@
         "license": "proprietary"
     },
     {
-        "id": "CIESIN_SEDAC_GHSL_PBSMOD_1.0",
+        "id": "CIESIN_SEDAC_GHSL_PBSMOD_1.00",
         "title": "Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid",
         "catalog": "SEDAC STAC Catalog",
         "state_date": "1975-01-01",
@@ -51904,7 +51618,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.html",
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         "description": "The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.",
         "license": "proprietary"
     },
@@ -51917,7 +51631,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1428079129-SEDAC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1428079129-SEDAC.html",
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         "description": "The Gridded Population of the World, Version 3 (GPWv3): Centroids consists of estimates of human population counts and densities for the years 1990, 1995, 2000, 2005, 2010, and 2015 by administrative Unit centroid location. The centroids are based on the 399,781 input administrative Units used in GPWv3. In addition to population counts and variables, the centroids have associated administrative Unit names and the land area of contained within the administrative Unit. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).",
         "license": "proprietary"
     },
@@ -51930,7 +51644,7 @@
         "bbox": "-179.199552, -54.751503, 179.848323, 79.89196",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000003-SEDAC.umm_json",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid estimates human population for the years 1990, 1995, and 2000 by 30 arc-second (1 km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).",
         "license": "proprietary"
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         "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid estimates population per square km for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values to grid cells. The population count grids are divided by the land area grid to produce population density grids. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).",
         "license": "proprietary"
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         "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points, Revision 01 is an updated version of the Settlement Points, Version 1 (v1) used in the original population reallocation. Revision 01 includes improved geospatial location for selected settlements, as well as new georeferenced settlements. Revision 01 was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the CUNY Institute for Demographic Research (CIDR).",
         "license": "proprietary"
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         "description": "The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points contains geospatial location for selected settlements. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).",
         "license": "proprietary"
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         "description": "The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.",
         "license": "proprietary"
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         "description": "The Global Summer Land Surface Temperature (LST) Grids, 2013, represent daytime maximum temperature and nighttime minimum temperature in degree Celsius at a spatial resolution of 30 arc-seconds (~1km) during summer months of the northern and southern hemisphere for the year 2013. The grids are produced using Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime LST 8-day composite data (MYD11A2). For most regions, the LST grids provide the daytime maximum (1:30 p.m. overpass) and nighttime minimum (1:30 a.m. overpass) LST values for each grid cell from a 40-day time-span during July-August (Julian days 185-224) 2013 in the northern hemisphere and January-February (Julian days 001-040) 2013 in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).",
         "license": "proprietary"
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         "description": "The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC\u00ef\u00bf\u00bds Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC\u00ef\u00bf\u00bds Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).",
         "license": "proprietary"
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@@ -54127,7 +53841,7 @@
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         "description": "The Global Urban Polygons and Points Dataset (GUPPD), Version 1 is a global data set of 123,034 urban settlements with place names and population for the years 1975-2030 in five-year increments. The data set builds on and expands the European Commission, Joint Research Centre's (JRC) 2015 Global Human Settlement (GHS) Urban Centre Database (UCDB). The JRC Settlement Model (GHS-SMOD) data set includes a hierarchy of urban settlements, from urban centre (level 30), to dense urban cluster (level 23), to semi-dense urban cluster (level 22). The UCDB only includes level 30, whereas the GUPPDv1 adds levels 23 and 22, and uses open data sources to both check and validate the names that JRC assigned to its UCDB polygons and to label the newly added settlements. The methodology described in the documentation was able to consistently label a greater percentage of UCDB polygons than were previously labeled by JRC.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The CYGNSS Level 1 Calibrated Raw IF Version 1.0 dataset is produced by the CYGNSS Science Team of the University of Michigan, and it contains the first release, Version 1.0, of the CYGNSS Calibrated Raw Intermediate Frequency (IF) based L1 Product. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.  <br><br>  This product includes several established signal coherence detectors, including the power-ratio P<sub>ratio</sub>, complex zero-Doppler delay waveform and full entropy E<sub>full</sub>, and a novel fast entropy detector E<sub>fast</sub>. Both entropy detectors are provided with two temporal resolutions: 2 ms and 50 ms. Several scattered signal strength products are included: Signal-to-Noise Ratio SNR, reflected power P<sub>g</sub>, reflectivity \u0393, and Normalized Bistatic Radar Cross-Section NBRCS.  Each of these products is derived using a coherent integration time of T<sub>c </sub>= 1 ms and incoherent integration times of N<sub>inc</sub> = 1000, 500, 250, 100, 50, and 2 ms. Signal strength time series at the shorter (2 and 50 ms) times provides excellent detection of land-water transitions in heterogeneous scenes. Delay Doppler Maps (DDMs) are also generated with high delay (\u2206\u03c4 = 1/16 chip) and Doppler (\u2206f= 50 Hz) resolution.  This suite of coherence detection methods can be used to detect the presence of small inland water bodies. ",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Version 1.0 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 2 months, depending on the availability of the MERRA wind speed reanalysis. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provide in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. The CDR exhibits improved calibration accuracy and stability over v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products  the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. By comparison, the v2.1 SDR L1 algorithm assumes a constant GPS transmit power, and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v2.1 SDR L1 data, so there is also some loss of samples that were present in v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Version 1.1 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 month, depending on the availability of the MERRA wind speed reanalysis. The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L1C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document.  Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.0 SDR L1 data, so there is also some loss of samples that were present in v3.0.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Version 1.2 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 week. The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L1C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document.  Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.1 SDR L1 data, so there is also some loss of samples that were present in v3.1.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Version 3.0 (v3.0) Full Delay Doppler Map (DDM) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), measures the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna) and processes them in real time into DDMs, which are two-dimensional maps of the signal scattered from the Earth surface as a function of propagation time delay and Doppler frequency shift. DDMs are normally sampled over a restricted range of delay and Doppler values centered on the values at the specular point of reflection. The bit resolution of scattered signal strength is also truncated by a lossy data compression algorithm. Full DDMs are sampled over a wider range of delay and Doppler values and retain their full (lossless) bit resolution. Full DDM data records are typically 10-15 min in duration and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest. This version supersedes the Full DDM Version 1.0 (https://doi.org/10.5067/CYGNS-L1FDD) for data retrieved during or after August 2018. For data retrieved prior to August 2018, users will need to continue using the Full DDM Version 1.0. This version links the Full DDMs to the CYGNSS v3.0 L1 files (https://doi.org/10.5067/CYGNS-L1X30) whereas the version 1.0 Full DDM linked the Full DDMs to the CYGNSS v2.1 L1 files (https://doi.org/10.5067/CYGNS-L1X21). The calibration of the Full DDMs has not been modified for this release.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Raw Intermediate Frequency (IF) sensor data from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The primary CYGNSS instrument, also known as the Delay-Doppler Mapping Instrument (DDMI), digitizes the incoming radio frequency (RF) streams from three input antenna channels (2 nadir oriented science antennas and one zenith oriented navigation antenna). The Raw IF data included in this data record are the raw sensor counts, retrieved prior to any digital signal processing, thus providing the highest possible resolution in delay and doppler space allowing for the construction of high resolution Delay Doppler Map (DDM) data. Raw IF data records are 30-90 sec in duration, with 60 sec being typical, and are initiated by ground commands to coincide with an overpass by one of the spacecraft of a target area of interest.",
         "license": "proprietary"
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         "description": "This Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) data is now available when the CYGNSS satellites are rolled away from nadir during orbital high beta-angle periods, resulting in a significant amount of additional data; 2) correction to coordinate frames result in more accurate estimates of receiver antenna gain at the specular point; 3) improved calibration for analog-to-digital conversion results in better consistency between CYGNSS satellites measurements at nearly the same location and time; 4) improved GPS EIRP and transmit antenna pattern calibration results in significantly reduced PRN-dependence in the observables;  5) improved estimation of the location of the specular point within the DDM; 6) an altitude-dependent scattering area is used to normalize the scattering cross section (v2.0 used a simpler  scattering area model that varied with incidence and azimuth angles but not altitude); 7) corrections added for noise floor-dependent biases in scattering cross section and leading edge slope of delay waveform observed in the v2.0 data. Users should also note that the receiver antenna pattern calibration is not applied per-DDM-bin in this v2.1 release.",
         "license": "proprietary"
     },
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         "description": "This Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21 . Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.0 data: 1) the transmitted GPS signal strength in the direction of the DDM scattering surface is determined in real time from measurements of the direct signal from the GPS satellite to the CYGNSS navigation receiver, allowing for the BRCS calibration to be corrected for variations in GPS transmit power; 2) the NBRCS has been validated using comparisons with a large population of modeled values derived from coincident ocean surface roughness spectra produced by the NOAA WAVEWATCH-3 oceanographic wave model; 3) L1 calibration parameters have been adjusted to produce a best fit to the model population.",
         "license": "proprietary"
     },
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         "description": "This Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.1 data: The CYGNSS science antenna gain patterns have been adjusted to improve the accuracy of the ocean surface scattering cross section (a.k.a. the NBRCS) calibration. They are adjusted so that the annual average observed NBRCS matches the model-predicted average as derived from Wavewatch-3 estimates of the surface roughness with the appropriate spectral tail extension added to the roughness spectrum. The adjustment is made independently at each position in the science antenna pattern. A correction for coarse quantization effects by the on-board digital processor has also been added. This reduces the effects of radio frequency interference, which appeared as calibration biases in the v3.0 L1 NBRCS and retrieval biases in the v3.0 L2 wind speed that were persistent at certain locations.",
         "license": "proprietary"
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         "description": "This CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. <br><br>  The correction for coarse quantization effects that was implemented in v3.1 for the signal portion of the DDM has been updated to include a correction to the noise floor portion of the DDM. This update is found to improve the sensitivity to soil moisture over land and to have a minimal effect on the sensitivity to wind speed over ocean. An update is made to the correction for the temperature dependence of the receiver electronics. This update reduces slow variations in calibration bias associated with a ~60 day oscillation in the mean temperature of the satellites. L1 variables over land and ocean are now combined in common netcdf data files, with additional details added regarding the specular point calculation over land. Nadir (science) antenna pattern and NBRCS rescaling has been updated to improve the inter-satellite consistency of the L1 calibration.<br><br>  The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 2 months (or better) from the last recorded measurement time. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products  the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.1 CDR represents is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L2X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L2C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.",
         "license": "proprietary"
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         "description": "This dataset contains the first release, Version 1.0, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR  v1.0 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.0 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C10.",
         "license": "proprietary"
     },
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         "description": "This dataset contains the first release, Version 1.1, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR  v1.1 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.1 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C11.",
         "license": "proprietary"
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         "description": "This dataset contains the third release, Version 1.2, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 6-7 day latency from the Delay Doppler Mapping Instrument (DDMI) aboard the Cyclone Global Navigation Satellite System (CYGNSS) constellation. CYGNSS is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.2 ocean surface wind speed estimates with the auxiliary parameters provided by the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS . Additional information on the CYGNSS L2 CDR v1.2 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C12 .",
         "license": "proprietary"
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         "description": "This dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Only one netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. Version 1.0 represents the first release. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. The 35 degree orbital inclination allows CYGNSS to measure ocean surface winds between approximately 38 degrees North and 38 degrees South latitude using an innovative combination of all-weather performance Global Positioning System (GPS) L-band ocean surface reflectometry to penetrate the clouds and heavy precipitation. The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), which uses data assimilation to combine all available in situ and satellite observation data with an initial estimate of the atmospheric state, provided by a global atmospheric model. Since the MERRA-2 data is only updated on monthly intervals, this corresponding heat flux dataset is likewise updated on a monthly interval to reflect the latest data available from MERRA-2, thus accounting for measurement latency, with respect to CYGNSS observables, ranging from 1 to 2 months. The data from this release compares well with in situ buoy data, including: Kuroshio Extension Observatory (KEO), National Data Buoy Center (NDBC), Ocean Sustained Interdisciplinary Time-series Environment observation System (OceanSITES), Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and the Tropical Atmosphere Ocean (TAO) array. As this marks only the first data release, future work is expected to provide comparisons and validation with various field campaigns (e.g., PISTON, CAMP2Ex) as well as more buoy data, especially at higher flux estimates.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 2.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 2.0 represents the second release of this product, which now uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.1 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). Version 1.0 used CYGNSS L2 SDR Version 2.1 surface wind speeds and NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. As a result of the CYGNSS constellation coverage, this data is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.",
         "license": "proprietary"
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         "description": "The CYGNSS level 2 ocean surface heat flux science data record version 3.2 dataset is provided as a service to the oceanographic and meteorological research communities on behalf of the CYGNSS Science Team in direct collaboration with the Cyclone Global Navigation Satellite System (CYGNSS) Mission. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.   <br><br>  This dataset provides time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Version 3.2 uses CYGNSS Level 2 (L2) Science Data Record (SDR) Version 3.2 surface wind speeds and ECMWF Reanalysis, Version 5 (ERA5). The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from this dataset obtains these values from ERA5. This dataset is made available from 1 August 2018 to present with an approximate 1 week latency in the netCDF-4 formatted data files, where each file contains data within a 24-hour UTC period from a combination of up to 8 unique CYGNSS spacecraft. More information on CYGNSS can be found on the CYGNSS mission page.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 2.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) revised Geophysical Model Functions (GMFs) for both Fully Developed Seas (FDS) and Young Seas with Limited Fetch conditions, to be consistent with the calibration changes made to the v2.1 Level 1 science data products.; 2) Revised covariance matrix between DDMA and LES versions of the FDS wind speed retrieval, used by the minimum variance estimator, resulting from changes made to the v2.1 Level 1 science data products; 3) Revised debiasing algorithm coefficients used by the FDS L2 retrieval algorithm, resulting from changes made to the v2.1 Level 2 science data products; 4) revised quality control (Q/C) flags related to the required level of consistency between DDMA and LES versions of the FDS wind speed retrieval (the errors in the two retrievals are now less correlated so larger discrepancies are allowed; if either retrieval is not available, the sample receives a fatal Q/C flag); 5) new Q/C flag related to the block type of the GPS satellite which provided the transmitted signal. Samples using block II-F signals receive a fatal Q/C flag due to the higher level of uncertainty in their radiated power; 6) revised wind speed uncertainty values as a function of RCG and wind speed, plus a new dependence of the uncertainty on GPS block type to reflect the higher uncertainty in GPS radiated power for block II-F satellites.",
         "license": "proprietary"
     },
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         "description": "This dataset contains the Version 3.0 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L2X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.0 data: 1) the changes to calibration and validation of the Level 1 Normalized Bistatic Radar Cross Section (NBRCS) necessitated updates to the Geophysical Model Functions (GMFs) used to retrieve wind speed; 2) the GMF for fully developed seas (FDS) conditions was generating using matchups between NBRCS measurements and coincident wind speeds produced by NASAs Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis model; 3) the GMF for young seas with limited fetch (YSLF) was generated using matchups between NBRCS and coincident wind speeds produced by NOAAs Hurricane Weather Research and Forecast (HWRF) System; 4) YSLF wind speed is a tapered linear combination of wind speeds derived from the FDS and YSLF GMFs, where the taper gives more weight to FDS at low wind speeds and more to YSLF at high wind speeds and accounts for the transition from FDS to YSLF sea state conditions near cyclonic storms; 5) re-introduces measurements using transmissions from previously discarded GPS satellite block types; in prior versions, Block II-F was completely discarded due to large variations in GPS transmit power. The real time transmit power monitoring and correction implemented in Level 1 v3.0 data now allows Block II-F signals to be used.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 3.1 CYGNSS Level 2 Science Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L2X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Here is a summary of processing changes reflected in the v3.1 data: The L2 Geophysical Model Functions (GMFs) that map L1 observables to ocean surface wind speed were rederived to be consistent with the v3.1 L1 calibration. The method used for deriving the GMFs is the same as for v3.0. A new correction has been added to both the Fully Developed Seas (FDS) and Young Seas Limited Fetch (YSLF) wind speed products that is a function of the Significant Wave Height (SWH) of the ocean surface. The correction is based on an observed correlation between the wind speed error and SWH. The SWH value used by the correction algorithm is the ERA5 reanalysis product, coincident in space and time with a CYGNSS measurement. The FDS and YSLF retrieval algorithms are otherwise the same as v3.0.",
         "license": "proprietary"
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         "description": "This dataset contains the version 3.2 CYGNSS level 2 science data record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L2X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. <br><br>    The L2 Geophysical Model Function (GMF) that maps L1 observables to ocean surface wind speed and the Significant Wave Height (SWH) second order correction to the wind speed retrievals were rederived to be consistent with the v3.2 L1 calibration. The method used for deriving the GMF and SWH correction is the same as for v3.1. An additional swell wave correction has been added to better account for the long wave dependence at low wind speeds. The FDS and YSLF retrieval algorithms are otherwise the same as v3.1. The v3.2 L2 YSLF wind speed is now designated as an intermediate product and should not be used \u2018as is\u2019. Additional quality control filters have been added to the Level 3 gridded product derived from the L2 YSLF wind speed to detect and remove outlier L2 samples, and use of the L3 product is recommended.<br><br>  The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882064-POCLOUD.html",
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         "description": "This dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products  the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205121540-POCLOUD.html",
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         "description": "This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2274918604-POCLOUD.html",
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         "description": "This dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 5 days latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L3X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L3C11 ), which was derived from SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.",
         "license": "proprietary"
     },
@@ -57273,7 +56987,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2142677420-POCLOUD.html",
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         "description": "The CYGNSS L3 Ocean Microplastic Concentration V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan.  CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.  <br><br>  This dataset contains the version 3.2 CYGNSS Level 3 ocean microplastic concentration data record, which provides daily netCDF files, each file containing a gridded map of microplastic number density (#/km^2). Microplastic concentration number density is indirectly estimated by an empirical relationship between ocean surface roughness and wind speed (Evans and Ruf, 2021). User caution is advised in regions containing independent, non-correlative factors affecting ocean surface roughness, such as anomalous atmospheric conditions within the Intertropical Convergence Zone, biogenic surfactants (such as algal blooms), oil spills, etc. This product reports microplastic concentration on a daily temporal and 0.25-degree latitude/longitude spatial grid with 30-day, 1 degree latitude/longitude feature resolution, as constrained by the binning and spatial temporal averaging of the Mean Square Slope (MSS) anomaly (i.e., difference between measured and predicted ocean surface roughness for a given wind speed). Version 3.2 uses CYGNSS MSS measurements that are derived from updated v3.2 Level 1 scattering cross section data and has updated the parameterizations in the data processing algorithm to use v3.2 data correctly.",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset contains the Version 1.0 Cyclone Global Navigation Satellite System (CYGNSS) Level 3 Storm Centric Grid (SCG) Science Data Record (SDR) which provides the average wind speed combined from aggregated wind speed measurements made by the entire CYGNSS constellation whose specular points are located near a storm of interest in latitude, longitude and time. Data are provided on both a 0.1x0.1 degree latitude by longitude equirectangular grid and storm centric coordinates obtained from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. Storm centric coordinates are derived from the National Hurricane Center (NHC) Best Track dataset to produce a 6 hourly wind speed averaging window. A single netCDF-4 data file is produced for each storm. Each storm is uniquely identified by the year, storm basin, and a storm number. This dataset is intended for historical storm analysis, and as such, this dataset is periodically updated based on the availability of the NHC Best Track storm center information that is typically made available in April for the previous year's hurricane season. SCG files are produced for named storms, as defined by the NHC, that reach hurricane strength (i.e., having a maximum sustained wind speed of at least 65 knots). Due to the dependency on NHC Best Track data, the SCG files produced in this dataset are confined to storms in the Northern Hemisphere within the North Atlantic and East Pacific ocean regions. Wind speed inputs are provided by the CYGNSS Level 2 SDR Version 3.0 (https://doi.org/10.5067/CYGNS-L2X30 ).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2205122332-POCLOUD.html",
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         "description": "The CYGNSS Level 3 Soil Moisture Product provides volumetric water content estimates for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics. The data were produced by CYGNSS investigators at the University Corporation for Atmospheric Research (UCAR) and the University Colorado at Boulder (CU), and derive from version 2.1 of the CYGNSS L1 SDR. The soil moisture algorithm uses collocated soil moisture retrievals from SMAP to calibrate CYGNSS observations from the same day. For a given location, a linear relationship between the SMAP soil moisture and CYGNSS reflectivity is determined and used to transform the CYGNSS observations into soil moisture. The data are archived in daily files in netCDF-4 format. Two soil moisture variables report the volumetric water content in units of cm3/cm3. The variable SM_subdaily includes up to four soil moisture estimates per day. Another variable SM_daily provides a daily average. The time series covers the period from March 2017 to present.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2927902887-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2927902887-POCLOUD.html",
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         "description": "The CYGNSS Level 3 Soil Moisture V3.2 dataset is provided by the CYGNSS Science Team of the University of Michigan. It estimates volumetric water content for soils between 0-5 cm depth at a 6-hour discretization for most of the subtropics from the V3.2 reflectivity measurements provided in the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X32). CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.  <br><br>  The soil moisture retrieval algorithm is an update of the previous version developed by UCAR-CU using a linear regression of CYGNSS angle-normalized effective surface reflectivity trained against collocated SMAP soil moisture during the calibration period 8/1/2018 to 11/15/2023. The data are archived in daily files in netCDF-4 format. Volumetric soil moisture water content in units of cm3/cm3 is provided with two gridding resolutions, 9x9 km and 36x36 km. The variable SM_subdaily contains data reported in six hour intervals. The variable SM_daily provides a daily average. The time series covers the period from August 2018 to present.",
         "license": "proprietary"
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         "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.   <br><br>  This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (<href>https://doi.org/10.5067/CYGNS-L1X32</href>). This is an update from the previous watermask monthly product (<href>https://doi.org/10.5067/CYGNS-L3W31</href>) which derived from the CYGNSS L1 SDR v3.1 (<href>https://doi.org/10.5067/CYGNS-L1X31</href>). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2928282019-POCLOUD.html",
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         "description": "The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.1 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours.    <br><br>  This dataset is derived from version 3.1 of the CYGNSS L1 SDR dataset (https://doi.org/10.5067/CYGNS-L1X31), and provides monthly binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with a 1-month latency. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in monthly files in netCDF-4 format and covers the period from August 2018 to present.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2251464874-POCLOUD.html",
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         "description": "This dataset contains the Version 3.0 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L3X21. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.0 release inherits all improvements made to the version 3.0 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.0 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X30.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2183149774-POCLOUD.html",
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         "description": "This dataset contains the Version 3.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L3X30. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 3.1 release inherits all improvements made to the version 3.1 Level 2 data intended to improve the quality of the wind speed retrievals. For a full list of improvements to the version 3.1 Level 2 data, please refer to: https://doi.org/10.5067/CYGNS-L2X31.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2832196567-POCLOUD.html",
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         "description": "This dataset contains the version 3.2 CYGNSS level 3 science data record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 3.1; https://doi.org/10.5067/CYGNS-L3X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). <br><br>  The v3.2 L3 gridded wind speed product inherits the v3.2 L2 FDS data as input at the same temporal and spatial resolution as the Level 2 data, sampled on consistent 0.2 by 0.2 degree latitude by longitude grid cells. The L3 gridding algorithm is unchanged. Range Corrected Gain (RCG) has been added to the L3 netcdf files as a new data field. <br><br>  The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38\u00b0 N and 38\u00b0 S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882072-POCLOUD.html",
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         "description": "This dataset contains the Version 1.1 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.1 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the first science-quality release produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported sample locations are determined by averaging the specular point locations falling within each 25 km grid cell. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2254232941-POCLOUD.html",
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         "description": "This dataset contains the Version 1.2 NOAA CYGNSS Level 2 Science Wind Speed Product Version 1.2 which provides the time-tagged and geolocated average wind speed (m/s) in 25x25 kilometer grid cells along the measurement tracks from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. This version corresponds to the second science-quality released through the PO.DAAC, as produced by NOAA/NESDIS using a specific geophysical model function (GMF version 1.0) and a track-wise debiasing algorithm as part of the wind speed retrieval process. The reported retrieval locations are determined by averaging the specular point locations falling within each 25 km grid cell. Version 1.2 includes four major updates compared to Version 1.1 ( https://doi.org/10.5067/CYGNN-22511 ), namely: 1) the inclusion of data associated to a spacecraft roll angle exceeding +/- 5 degrees; 2) an improved wind speed performance in the higher wind speed regime; 3) a full revision of the quality flags; 4) the inclusion of a wind speed retrieval error variable. Only one netCDF-4 data file is produced for each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. Formatting of the data variables and metadata designed to be consistent with the netCDF-4 formatting provided by the legacy CYGNSS mission Level 2 wind speed science data record (SDR).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036881727-POCLOUD.html",
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid.  The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites.  The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Visible Infrared Imager Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice.  This dataset adheres to the version 2 GHRSST Data Processing Specification (GDS).",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2096684707-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2096684707-POCLOUD.html",
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         "description": "This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds].",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1990404801-POCLOUD.html",
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         "description": "This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
     },
@@ -62278,7 +61992,7 @@
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         "description": "This dataset provides monthly-averaged Gent-McWilliams ocean bolus ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean and sea-ice surface freshwater fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides geometric parameters for the regular 0.5-degree lat-lon grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides and the horizontal and vertical coordinates of grid cell centers and corners. Additional information related to the global domain geometry (e.g., bathymetry and land/ocean masks) are also included. However, users should note these domain geometry fields are approximations because they have been interpolated from the ECCO lat-lon-cap 90 (llc90) native model grid. Users interested in exact budget closure calculations for volume, heat, salt, or momentum should use ECCO fields provided on the llc90 grid. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides; horizontal and vertical coordinates of grid cell centers and corners; grid rotation angles; and global domain geometry including bathymetry and land/ocean masks. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides instantaneous hourly global mean atmospheric pressure from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
     },
@@ -62512,7 +62226,7 @@
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         "description": "This dataset contains monthly-averaged ocean and sea-ice surface heat fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean and sea-ice surface heat fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides monthly-averaged ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides instantaneous ocean bottom pressure and model ocean bottom pressure anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides 3D coefficients for the Gent-McWilliams and Redi parameterizations and background vertical diffusivity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Each of these three time-invariant, spatially-varying terms are estimated during the ECCO V4r4 optimization. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
     },
@@ -62785,7 +62499,7 @@
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         "description": "This dataset provides daily-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean three-dimensional momentum tendency on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean three-dimensional potential temperature fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean three-dimensional volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides instantaneous hourly SBO core products from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged sea-ice and snow horizontal volume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged sea-ice salt plume fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides instantaneous sea-ice velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "This dataset provides instantaneous dynamic sea surface height and model sea level anomaly on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include dynamic sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; dynamic sea surface temperature (SST) from satellite radiometers [AVHRR], dynamic sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged ocean and sea-ice surface stress interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean and sea-ice surface stress on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset contains monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides daily-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
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         "description": "This dataset provides instantaneous ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO1BATT Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS. The data are provided in 1 second intervals by the ISS, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data.    The ECO1BATT Version 1 data product contains layers of corrected and uncorrected attitude quaternions, spacecraft ephemeris data including Earth-centered inertial (ECI) position and velocity, and associated time elements. ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO1BGEO Version 1 data product provides the geolocation information for the radiance values retrieved in the ECO1BRAD Version 1 data product. The ECO1BGEO data product should be used to georeference the ECO1BRAD, ECO2CLD, ECO2LSTE, ECO3ANCQA, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO1BGEO data product is provided as swath data.    The ECO1BGEO data product contains data layers for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water.  ",
         "license": "proprietary"
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@@ -63513,7 +63227,7 @@
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO1BMAPRAD Version 1 data product combines the at-sensor calibrated radiance values retrieved for the ECO1BRAD data product and the geolocation information provided in the ECO1BGEO data product to produce a geotagged, resampled radiance product. The ECO1BMAPRAD data product is produced as a map registered product that is in a rotated geographic projection with a spatial resolution of 70 meters (m). The ECO1BMAPRAD data product accounts for the overlap and variable pixel size in the ECO1BRAD data product.    The ECO1BMAPRAD Version 1 data product contains data layers including the radiance values for the five thermal infrared (TIR) bands, digital number (DN) values for the shortwave infrared (SWIR) band, associated data quality indicators, latitude and longitude values, solar and view geometry information, and surface height.    ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO1BRAD Version 1 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) to 90 m at the edge of the swath. The ECO1BRAD data product is provided as swath data and does not contain geolocation information. The corresponding ECO1BGEO data product is required to georeference the ECO1BRAD data product. The geographic coverage of acquisitions for the ECO1BRAD Version 1 data product extends to areas outside of those indicated on the coverage map. However, corresponding higher-level products over these areas are not available at this time.    The ECO1BRAD Version 1 data product contains layers of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO2CLD Version 1 data product provides a cloud mask that can be used to determine cloud cover for the ECO1BRAD, ECO2LSTE, ECO3ETPTJPL, ECO4ESIPTJPL, and ECO4WUE data products. The ECOSTRESS Level 2 cloud product is derived using the five calibrated thermal bands in a multispectral cloud-conservative thresholding approach. The details of the algorithm are provided in the Algorithm Theoretical Basis Document (ATBD). The corresponding ECO1BGEO data product is required to georeference the ECO2CLD data product.    The ECO2CLD Version 1 data product contains a single cloud mask layer. Information on how to interpret the bit fields in the cloud mask is provided in section 3.1 of the User Guide.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website.    The ECO2LSTE Version 1 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO2LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO2LSTE is provided as swath data and has a spatial resolution of 70 meters (m). The corresponding ECO1BGEO data product is required to georeference the ECO2LSTE data product.    The ECO2LSTE Version 1 data product contains layers of LST, emissivity for bands 1 through 5, quality control for LST&E, LST error, emissivity error for bands 1 through 5, wideband emissivity, and Precipitable Water Vapor (PWV).",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product.    The ECO3ANCQA Version 1 data product contains layers of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The United States Department of Agriculture (USDA) ECO3ETALEXIU Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXIU data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (disALEXI). Described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/332/ECO3ETALEXIU_ATBD_V1.pdf), disALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. Many approaches exist for mapping ET spatially; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. The output ET is generated on a UTM grid at a spatial resolution of 30 meters.    The ECO3ETALEXIU Version 1 data product contains layers of daily ET, ET uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018 and moved to autonomous science operations on August 20, 2018 following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019 through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020 an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020 due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The NASA Jet Propulsion Laboratory (JPL) ECO3ETALEXI Version 1 data product provides estimates of daily evapotranspiration (ET) using the ECOSTRESS Level 2 (L2) land surface temperature and emissivity (LST&E) product, along with ancillary meteorological data and remotely sensed vegetation cover information. The ECO3ETALEXI data product is derived using a physics-based surface energy balance (SEB) algorithm, the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI). Described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1000/ECO3ETALEXI_ATBD_V1.pdf), DisALEXI is based on spatial disaggregation of regional-scale fluxes from the ALEXI SEB model. There are many approaches for spatially mapping ET; however, SEB methods are favored for remote sensing retrievals based on land-surface temperature. ALEXI was initially developed for managed landscapes and has now been evaluated in comparison with micrometeorological flux tower observations over crop, forest, grassland, wetland, and semiarid desert sites. Applications include crop water use, crop phenology monitoring, and drought early warning or water stress detection. ECO3ETALEXI is available for CONUS at 70-meter (m) pixel resolution.    The ECO3ETALEXI Version 1 data product contains layers of daily ET, ET uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ET as a stretched image with a color ramp in JPEG format.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    ECO3ETPTJPL Version 1 is a Level 3 (L3) product that provides evapotranspiration (ET) generated from data acquired by the ECOSTRESS radiometer instrument according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/335/ECO3ETPTJPL_ATBD_V1.pdf). The ET product is generated from the Level 2 data products for surface temperature and emissivity, the Level 1 geolocation information, and a significant number of ancillary data inputs from other sources.  ET is set by various controls, including radiative and atmospheric demand, and environmental sensitivity, productivity, vegetation physiology, and phenology. PT-JPL is best utilized for natural ecosystems. The L3 ET product is used for creating the Level 4 products, Evaporative Stress Index (ESI) and Water Use Efficiency (WUE).    The ECO3ETPTJPL Version 1 data product contains layers of instantaneous ET, daily ET, canopy transpiration, soil evaporation, ET uncertainty, and interception evaporation.     Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The United States Department of Agriculture (USDA) ECO4ESIALEXIU Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/340/ECO4ESIALEXIU_ATBD_V1.pdf). ESI applications include indicating agricultural drought and observing vegetation stress. The dis-ALEXI USDA ESI product is generated on a UTM grid at a spatial resolution of 30 meters.    The ECO4ESIALEXIU Version 1 data product contains layers of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The NASA Jet Propulsion Laboratory (JPL) ECO4ESIALEXI Version 1 data product provides the Evaporative Stress Index (ESI), which is computed from clear-sky estimates of the relative daily evapotranspiration (ET) fraction: ESI = ET/ETo, where ET is ETdaily from the ECOSTRESS Level 3 product and ETo is the reference ET. A description of the major components of the ECOSTRESS algorithm implemented in Version 1 of the Atmosphere Land Exchange Inverse (ALEXI) Disaggregation algorithm (DisALEXI) ESI code is provided in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1002/ECO4ESIALEXI_ATBD_V1.pdf). ESI applications include indicating agricultural drought and observing vegetation stress. ECO4ESIALEXI is available for CONUS at 70-meter (m) pixel resolution.    The ECO4ESIALEXI Version 1 data product contains layers of daily evaporative stress index, evaporative stress index uncertainty, and associated quality flags. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only TIR bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The ECO4ESIPTJPL Version 1 data product provides Evaporative Stress Index (ESI) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 (ESI_PT-JPL) Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/342/ECO4ESIPTJPL_ATBD_V1.pdf). The ESI product is derived from the ratio of the Level 3 actual evapotranspiration (ET) to potential ET (PET) calculated as part of the algorithm. The ESI is an indicator of potential drought and plant water stress emphasizing areas of sub-optimal plant productivity.    The ECO4ESIPTJPL Version 1 data product contains layers of ESI and PET.    Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science)    The ECO4WUE Version 1 data product provides Water Use Efficiency (WUE) data generated according to the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the ECOSTRESS Level 4 WUE Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/346/ECO4WUE_ATBD_V1.pdf). WUE is the ratio of carbon stored by plants to water evaporated by plants. This ratio is given as grams of carbon stored per kilogram of water evaporated over the course of the day from sunrise to sunset on the day when the ECOSTRESS granule was acquired.    The ECO4WUE Version 1 data product contains a single layer of water use efficiency.     Known Issues: *Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).  The ECOSTRESS Swath Geolocation Instantaneous Level 1B Global (ECO_L1B_GEO) Version 2 data product provides the geolocation information for the radiance values retrieved in the ECO_L1B_RAD (https://doi.org/10.5067/ecostress/eco_l1b_rad.002) Version 2 data product. The geolocation product gives geo-tagging to each of the radiance pixels. The geolocation processing corrects the ISS-reported ephemeris and attitude data by image matching with a global ortho-base derived from Landsat data, and then assigns latitude and longitude values to each of the Level 1 radiance pixels. When image matching is successful, the data are geolocated to better than 50 meter (m) accuracy. The ECO_L1B_GEO data product is provided as swath data.  The ECO_L1B_GEO data product contains data layers for latitude and longitude values, solar and view geometry information, surface height, and the fraction of pixel on land versus water distributed in HDF5 format.    Known Issues: *Geolocation accuracy: In cases where scenes were not successfully matched with the ortho-base, the geolocation error is significantly larger; the worst-case geolocation error for uncorrected data is 7 kilometers (km). Within the metadata of the ECO_L1B_GEO file, if the field \"L1GEOMetadata/OrbitCorrectionPerformed\" is \"True\", the data was corrected, and geolocation accuracy should be better than 50 m. If this field is \"False\", then the data was processed without correcting the geolocation and will have up to 7 km geolocation error.  *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: EECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).  The ECOSTRESS Swath Top of Atmosphere Calibrated Radiance Instantaneous L1B Global 70 m (ECO_L1B_RAD) Version 2 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) and 90 m at the edge of the swath. The ECO_L1B_RAD data product is provided as swath data and does not contain geolocation information. The corresponding ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L1B_RAD data product. The geographic coverage of acquisitions for the ECO_L1B_RAD Version 2 data product extends to areas outside of those indicated on the coverage map.   The ECO_L1B_RAD Version 2 data product contains layers of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data distributed in HDF5 format.    Known Issues: *Cannot perform spatial query on ECO_L1B_RAD in NASA Earthdata Search: ECO_L1B_RAD does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO_L1B_RAD data products by orbit number instead.  *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Missing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the User Guide.  *Scan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the User Guide.   *Scan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the User Guide.   *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).  The ECOSTRESS Tiled Top of Atmosphere Calibrated Radiance Instantaneous Level 1 Global 70 m (ECO_L1CT_RAD) Version 2 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 \u00b5m. This tiled data product is generated from the ECO_L1CG_RAD (https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data product using a modified version of the Military Grid Reference System (MGRS) (https://hls.gsfc.nasa.gov/products-description/tiling-system/), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each ECOSTRESS pixel can be assumed to remain at the same location at each timestep within a tile.  The ECO_L1CT_RAD Version 2 data product contains 12 layers distributed in Cloud Optimized GeoTIFF (COG) format consisting of separate files containing five TIR bands, associated data quality indicators, and cloud and water masks.     Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Missing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the ECO_L1B_RAD User Guide.  *Scan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the ECO_L1B_RAD User Guide.  *Scan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the ECO_L1B_RAD User Guide.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).  The ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO_L2T_LSTE data product was derived using a physics-based Temperature/Emissivity Separation (TES) algorithm. This tiled data product is subset from the ECO_L2G_LSTE data product using a modified version of the Military Grid Reference System (MGRS) which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.  The ECO_L2T_LSTE Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains seven layers including LST, LST error, wideband emissivity, quality flags, height, and cloud and water masks.    Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) and Harmonized Landsat Sentinel (HLS) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.    The ECO_L2T_STARS Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each band is distributed as a separate COG. This product contains four layers including NDVI, NDVI uncertainty, albedo, and albedo uncertainty. The ECO_L2T_STARS ancillary NDVI and albedo product is only generated for corresponding daytime ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) Version 2 tiles.    The ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS) Version 2 data product provides Normalized Difference Vegetation Index (NDVI) and albedo aligned with each daytime ECOSTRESS overpass. ECO_L2T_STARS is an ancillary data product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) ( https://lpdaac.usgs.gov/product_search/?query=viirs&status=Operational&view=list&sort=title) and Harmonized Landsat Sentinel (HLS) ( https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) Version 2 data sources with application to the ECOSTRESS mission. This data product fuses fine resolution inputs from HLS surface reflectance products and moderate resolution inputs from the daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS surface reflectance (VNP09GA) ( https://doi.org/10.5067/VIIRS/VNP09GA.001) product. The data fusion is performed using a variant of the Spatial Timeseries for Automated high-Resolution multi-Sensor data fusion (STARS) algorithm to create tiles matching the ECOSTRESS standard resolution of 70 meters (m). STARS is a Bayesian timeseries methodology that provides streaming data fusion and uncertainty quantification through efficient Kalman filtering. Refer to Section 3.1 of the ECOSTRESS Jet Propulsion Laboratory (JPL) EvapoTranspiration (JET) Level-3 Algorithm Theoretical Basis Document (ATBD) for further details of the Bidirectional Reflectance Distribution Function (BRDF) implementation and albedo calculations.    The ECO_L2T_STARS Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format using a modified version of the Military Grid Reference System (MGRS) ( https://hls.gsfc.nasa.gov/products-description/tiling-system/), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. Each band is distributed as a separate COG. This product contains four layers including NDVI, NDVI uncertainty, albedo, and albedo uncertainty. The ECO_L2T_STARS ancillary NDVI and albedo product is only generated for corresponding daytime ECOSTRESS Tiled Land Surface Temperature and Emissivity Instantaneous Level 2 Global 70 m (ECO_L2T_LSTE) ( https://doi.org/10.5067/ECOSTRESS/ECO_L2T_LSTE.002) Version 2 tiles.     ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).    The ECOSTRESS Swath Cloud Mask Instantaneous L2 Global 70 m (ECO_L2_CLOUD) Version 2 data product is derived using a single-channel Bayesian cloud threshold with a look-up-table (LUT) approach. The ECOSTRESS Level 2 cloud product provides a cloud mask that can be used to determine cloud cover for accurate land surface temperature and evapotranspiration estimation. The corresponding ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L2_CLOUD data product.     The ECO_L2_CLOUD Version 2 data product contains two cloud mask layers: Brightness temperature LUT test and Final cloud mask. Information on how to interpret the bit fields in the cloud mask is provided in Table 7 of the User Guide.    Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in figure 2 on the ECOSTRESS website(https://ecostress.jpl.nasa.gov/science).  The ECOSTRESS Swath Land Surface Temperature and Emissivity Instantaneous L2 Global 70 m (ECO_L2_LSTE) Version 2 data product provides atmospherically corrected land surface temperature and emissivity (LST&E) values derived from five thermal infrared (TIR) bands. The ECO_L2_LSTE data product was derived using a physics-based Temperature and Emissivity Separation (TES) algorithm. The ECO_L2_LSTE is provided as swath data and has a spatial resolution of 70 meters (m). The corresponding  ECO_L1B_GEO (https://doi.org/10.5067/ECOSTRESS/ECO_L1B_GEO.002) data product is required to georeference the ECO_L2_LSTE data product.  The ECO_L2_LSTE Version 2 data product contains layers of LST, emissivity for bands 1 through 5, quality control for LST&E, LST error, emissivity error for bands 1 through 5, wideband emissivity, Precipitable Water Vapor (PWV), cloud mask, and water mask.    Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.  *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.  *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).    The ECOSTRESS Tiled Evapotranspiration Instantaneous and Daytime L3 Global 70 m (ECO_L3T_JET) Version 2 data product provides instantaneous canopy transpiration, leaf surface evaporation, and soil moisture evaporation using the Priestley-Taylor formula. This data product is tiled using a modified version of the Military Grid Reference System (MGRS (https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.    The ECO_L3T_JET Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains 12 layers including ETdaily, ETinstUncertainty, PTJPLSMinst, STICinst, MOD16inst, BESSinst, STICcanopy, PTJPLSMcanopy, PTJPLSMinterception, PTJPLSMsoil, cloud mask, and water mask.  ",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).    The ECOSTRESS Tiled Downscaled Meteorology Instantaneous L3 Global 70 m (ECO_L3T_MET) Version 2 data product provides instantaneous near-surface air temperature (Ta) and relative humidity (RH) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and Ta or RH from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled meteorology estimates are recorded into the ECO_L3T_MET data product and tiled using a modified version of the Military Grid Reference System (MGRS (https://hls.gsfc.nasa.gov/products-description/tiling-system/)) which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.    The ECO_L3T_MET Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format with each data layer distributed as a separate COG. This product contains four layers including Ta, RH, cloud mask, and water mask.",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found in Figure 2 on the ECOSTRESS website (https://ecostress.jpl.nasa.gov/science).    The ECOSTRESS Tiled Surface Energy Balance Instantaneous L3 Global 70 m (ECO_L3T_SEB) Version 2 data product provides estimated incoming surface radiation (Rg) and net radiation (Rn) aligned with each daytime ECOSTRESS overpass. The Rg was generated using the Forest Light Environmental Simulator (FLiES) radiative transfer model implemented in an artificial neural network using Cloud Optical Thickness (COT) and Aerosol Optical Thickness (AOT) from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) along with albedo from ECOSTRESS Tiled Ancillary NDVI and Albedo Level 2 Global 70 m (ECO_L2T_STARS(https://doi.org/10.5067/ECOSTRESS/ECO_L2T_STARS.002)) Version 2 as variables. The Rg output from the FLiES model was bias corrected to Rg from GEOS-FP. The Rn is an output from the Breathing Earth System Simulator (BESS) algorithm. This data product is tiled using a modified version of the Military Grid Reference System (MGRS(https://hls.gsfc.nasa.gov/products-description/tiling-system/)), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution.    The ECO_L3T_SEB Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format with each data layer distributed as a separate COG. This product contains four layers including Rg, Rn, cloud mask, and water mask.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2074860916-LPCLOUD.html",
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.  The ECOSTRESS Tiled Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3T_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled soil moisture estimates are recorded into the ECO_L3T_SM data product and tiled using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.  The ECO_L3T_SM Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains three layers including SM, cloud mask, and water mask.    Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
     },
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the  ECOSTRESS website.    The ECOSTRESS Tiled Evaporative Stress Index PT-JPL Instantaneous L4 Global 70 m (ECO_L4T_ESI) Version 2 data product uses the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) model to generate estimates of both actual and potential instantaneous evapotranspiration (ET). The potential evapotranspiration (PET) estimate represents the maximum expected ET if there were no water stress to plants on the ground. The ratio of the actual ET estimate to the PET estimate forms an index representing the water stress of plants.    The ECO_L4T_ESI Version 2 data product is available globally in Cloud Optimized GeoTIFF (COG) format and uses a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each granule contains separate COG files for each layer: Evaporative Stress Index (ESI), PET, and cloud mask, and water mask. A low-resolution browse is also available showing daily ESI as a stretched image with a color ramp in JPEG format.     Known Issues:  *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. *Data acquisition gap:  From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
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         "description": "The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52\u00b0 N and 52\u00b0 S latitudes. A map of the acquisition coverage can be found on the ECOSTRESS website.    The ECOSTRESS Tiled Water Use Efficiency Instantaneous L4 Global 70 m (ECO_L4T_WUE) Version 2 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE, the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants.     The ECO_L4T_WUE Version 2 data product is available globally in Cloud Optimized GeoTIFF (COG) format and uses a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. Each granule contains separate COG files for each layer: Water Use Efficiency (WUE), Water Gross Primary Production, cloud mask, and water mask. A low-resolution browse is also available showing WUE as a stretched image with a color ramp in JPEG format.     Known Issues: *Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. *Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. *Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.",
         "license": "proprietary"
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS). During its one-year mission, EMIT will take mineralogical measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. A map of the regions being investigated can be found on the EMIT website. The EMIT Level 1B Corrected Spacecraft Attitude and Ephemeris (EMITL1BATT) Version 1 data product provides both corrected and uncorrected attitude quaternions and spacecraft ephemeris data obtained from the ISS, including Earth-centered inertial (ECI) position and velocity, and associated time elements. The data are provided in 1 second intervals, and each product file contains vectors from the duration of the orbit. The time elements are copied from the ISS raw data. The data for each EMITL1BATT granule are delivered in a single Network Common Data Format 4 (netCDF-4) file.",
         "license": "proprietary"
     },
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.  The EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation (EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. Each EMITL1BRAD granule consists of two Network Common Data Format Version 4 (netCDF-4) files at a spatial resolution of 60 meters (m): Radiance (EMIT_L1B_RAD) and Observation (EMIT_L1B_OBS). The Radiance file contains the at-sensor radiance measurements of 285 bands with a spectral range of 381-2493 nanometers (nm) and with a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Observation file contains viewing and solar geometries, timing, topographic, and other information related to the observation.  Each NetCDF4 file holds a location group containing geometric lookup tables (GLT), which are orthorectified images that provide relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT\u2019s spatial resolution.  Each granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.",
         "license": "proprietary"
     },
@@ -64228,7 +63942,7 @@
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    The EMIT Level 2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) Version 1 data product provides surface reflectance data in a spatially raw, non-orthocorrected format. Each EMITL2ARFL granule consists of three Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Reflectance (EMIT_L2A_RFL), Reflectance Uncertainty (EMIT_L2A_RFLUNCERT), and Reflectance Mask (EMIT_L2A_MASK). The Reflectance file contains surface reflectance maps of 285 bands with a spectral range of 381-2493 nanometers (nm) at a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Reflectance Uncertainty file contains uncertainty estimates about the reflectance captured as per-pixel, per-band, posterior standard deviations. The Reflectance Mask file contains six binary flag bands and two data bands. The binary flag bands identify the presence of features including clouds, water, and spacecraft which indicate if a pixel should be excluded from analysis. The data bands contain estimates of aerosol optical depth (AOD) and water vapor.    Each NetCDF4 file holds a location group containing a geometric lookup table (GLT) which is an orthorectified image that provides relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files will also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT\u2019s spatial resolution.    Each granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Methane Enhancement Data (EMITL2BCH4ENH) Version 1 data product is a total vertical column enhancement estimate of methane in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCH4ENH provides per-pixel methane enhancement data used to identify methane plume complexes. The initial release of the EMITL2BCH4ENH data product will only include granules where methane plume complexes have been identified. Each granule contains one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m): Methane Enhancement (EMIT_L2B_CH4ENH). The EMITL2BCH4ENH file contains methane enhancement data based primarily on EMITL1BRAD radiance values.    Each granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Estimated Methane Plume Complexes (EMITL2BCH4PLM) Version 1 data product provides estimated methane plume complexes in parts per million meter (ppm m) along with uncertainty data. The EMITL2BCH4PLM data product will only be generated where methane plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCH4ENH data undergo a manual review (or identification and confirmation) process before being designated as a plume complex. For more information on the manual review process, see Section 4.2.2 of the EMIT GHG Algorithm Theoretical Basis Document (ATBD). Each EMITL2BCH4PLM granule is sized to a specific plume complex but may cross multiple EMITL2BCH4ENH granules. A list of source EMITL2BCH4ENH granules is included in the GeoTIFF file metadata as well as the GeoJSON file. Each EMITL2BCH4PLM granule contains two files: one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m) and one GeoJSON file. The EMITL2BCH4PLM COG file contains a raster image of a methane plume complex extracted from EMITL2BCH4ENH v001 data. The EMITL2BCH4PLM GeoJSON file contains a vector outline of the plume complex, a list of source scenes, coordinates of the maximum enhancement values, and the uncertainty of the plume complex.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize carbon dioxide CO2 and methane CH4 point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 1 data product is a total vertical column enhancement estimate of CO2 in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel CO2 enhancement data used to identify CO2 plume complexes. The initial release of the EMITL2BCO2ENH data product will only include granules where CO2 plume complexes have been identified. Each granule contains one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH). The EMITL2BCO2ENH COG file contains methane enhancement data based primarily on EMITL1BRAD radiance values.    Each granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the locations of methane plumes along with metadata, regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize carbon dioxide CO2 and methane CH4 point-source emissions by measuring gas absorption features in the short-wave infrared bands. The EMIT Level 2B Greenhouse Gas (GHG) series of products can be used to identify and quantify point source emissions. The EMIT Level 2B Estimated Carbon Dioxide Plume Complexes (EMITL2BCO2PLM) Version 1 data product provides estimated carbon dioxide plume complexes in parts per million meter (ppm m) along with uncertainty data. The EMITL2BCO2PLM data product will only be generated where carbon dioxide plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCO2ENH data undergo a manual review (or identification and confirmation) process before being designated as a plume complex. For more information on the manual review process, see Section 4.2.2 of the EMIT GHG Algorithm Theoretical Basis Document (ATBD). Each EMITL2BCO2PLM granule is sized to a specific plume complex but may cross multiple EMITL2BCO2ENH granules. A list of EMITL2BCO2ENH source granules is included in the GeoTIFF file metadata as well as the GeoJSON file. Each EMITL2BCO2PLM granule contains two files: one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m) and one GeoJSON file. The EMITL2BCO2PLM COG file contains a raster image of a carbon dioxide plume complex extracted from EMITL2BCO2ENH v001 data. The EMITL2BCO2PLM GeoJSON file contains a vector outline of the plume complex, a list of source scenes, coordinates of the maximum enhancement values, and the uncertainty of the plume complex.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.    The EMIT Level 2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) Version 1 data product provides estimated mineral identification and band depths in a spatially raw, non-orthocorrected format. Each EMITL2BMIN granule contains two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Mineral Identification (EMIT_L2B_MIN) and Mineral Uncertainty (EMIT_L2B_MINUNCERT). The EMIT_L2B_MIN file contains the band depth (the depth of the identified spectral feature) and the identified mineral for each pixel. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur. These estimates are generated using the Tetracorder system (code) and are based on EMITL2ARFL reflectance values. The EMIT_L2B_MINUNCERT file provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the EMITL2ARFL data product. The band depth uncertainties are presented as standard deviations. The fit score for each mineral identification is also provided as the coefficient of determination (r2) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories is available in the emit-sds-l2b repository and will be available as subsequent data products.    The EMITL2BMIN data product includes a total of 19 Science Dataset (SDS) layers. There are four layers for each of the Spectral Groups (Group 1 and Group 2): Mineral Identification, Band Depth, Band Depth Uncertainties, and Fit Score. Additional layers consist of geometric lookup table (GLT) x values, GLT y values, latitude, longitude, elevation, associated spectral library record, mineral name, URL for the spectral library description, spectral group, spectral library, and spectral group index. A browse image with Group 1 Band Depth, Group 2 Band Depth, Group 1 Band Depth Uncertainty, and Group 2 Band Depth Uncertainty is also included.    Each granule is approximately 75 kilometer (km) by 75 km, nominal at the equator, and some granules near the end of an orbit segment reaching 150 km in length.    Disclaimer  This product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the Algorithm Theoretical Basis Document (ATBD) for more details.",
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.  The EMIT Level 3 Aggregated Mineral Spectral Abundance and Uncertainty (EMITL3ASA) Version 1 data product provides an aggregated mineral spectral abundance of the 10 minerals that are the focus of the EMIT mission. These minerals, referred to as the EMIT-10 minerals, are calcite, chlorite, dolomite, goethite, gypsum, hematite, illite+muscovite, kaolinite, montmorillonite, and vermiculite. The EMITL3ASA granule consists of one network Common Data Format 4 (netCDF-4) file at a spatial resolution of 0.5 degrees. The data in EMITL3ASA relies heavily on the EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) data. Using the EMITL2BMIN data, aggregated spectral abundance (ASA) is calculated for each of the EMIT-10 minerals as the simple average of relevant 60 m pixels within each 0.5 degree grid cell in the EMITL3ASA product, after controlling for the estimated fractional cover of bare soil within the pixel.   The EMITL3ASA data product contains 20 Science Dataset (SDS) layers. There are two layers for each of the EMIT-10 minerals: mineral spectral abundance and mineral spectral abundance uncertainty. The latitude and longitude layers contain the coordinates for the upper left corner of each pixel.",
         "license": "proprietary"
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         "description": "The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth\u2019s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52\u00b0 N latitude and 52\u00b0 S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.     The EMIT Level 4 Earth System Model (EMITL4ESM) Version 1 data product provides radiative forcing outputs, along with other ancillary outputs generated from different Earth System Models (ESMs). ESMs are complex models that integrate relevant physical, chemical, biological, and human components to simulate multiple aspects of large-scale systems on Earth. Multiple models, input mineral maps, meteorology inputs, and emissions/concentration scenarios are examined for the model runs contained within this data product. Models currently utilized include the Community Earth System Model 2 (CESM2) and the Goddard Institute for Space Studies (GISS) model. Some ESM runs utilize reference surface mineral maps from the literature dating back to 2007; others rely on the EMIT L3 Aggregated Mineral Spectral Abundance and Uncertainty 0.5 Deg (EMITL3ASA) data as inputs.     Each EMITL4ESM granule represents a single ESM run with a Network Common Data Format 4 (netCDF-4) file for each variable. A total of 12 Science Dataset (SDS) layers or variables are provided for each model run. For some SDS layers or variables, multiple layers based on inclusion of model minerology inputs are provided in their netCDF files. The layers/variables table below details which variables contain the extra layers. Metadata flags for Earth System Model, Resolution, Surface Mineral Map, External Meteorology, Time Period, and Emissions/Concentration Scenario indicate the key parameters for each granule. A table outlining each variable in detail can be found in the EMIT Science Data System Level 4 repository. ",
         "license": "proprietary"
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         "description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-1 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP.",
         "license": "proprietary"
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         "description": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-2 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP. For more information, please visit: http://www.scp.byu.edu/docs/ERS_user_notes.html",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P sea surface temperature produced by The Naval Oceanographic Office (NAVO) from the GOES Imager sensor on the Electro-Optical Infrared Weather System \u2013 Geostationary satellite (EWS-G1). The EWS-G1, formerly GOES-13, is the first Department of Defense owned geostationary weather satellite, which has been repositioned over Indian Ocean (IO) region at 60.0\u00b0 West longitude in January 2018 and fully operational since September 8, 2020, providing timely cloud characterization and theater weather imagery to DoD. The EWS-G1 L2P SST product is calculated based on the 4-micron (band 2) and 11-micron (band 4) channels, providing nighttime and daytime SST. However, daytime SSTs are not produced in areas where the 4-micron channel is strongly affected by Solar radiation, which is defined by solar reflection angle > 50 degree. The L2P data are packaged according to the GHRSST Data Specification version 2 (GDS2) in netCDF4 format at 0.04-degree spatial resolution and stored in 48 half-hour granules per day. The data will be continually updated with 24 hours latency. ",
         "license": "proprietary"
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         "description": "The G18-ABI-L2P-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI onboard the G18 satellite. NOAA\u2019s G18 (aka, GOES-T pre-launch) was launched on March 1, 2022, replacing the G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.  <br><br>  G18/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with a spatial resolution of 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P is produced in netCDF4 GDS2 format, with 24 granules per day, and a total data volume 0.8 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).  <br><br>  Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script available at Documents tab under How-To section. The ACSPO G18 ABI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/G18-ABI-L3C-ACSPO-v2.90     ",
         "license": "proprietary"
     },
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         "description": "The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA\u2019s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES\u2013R Series, the Western Hemisphere\u2019s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth\u2019s weather, oceans, and environment.  <br><br>  The G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.  <br><br>  The ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).       ",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.    The GEDI Level 1B Geolocated Waveforms product (GEDI01_B) provides geolocated corrected and smoothed waveforms, geolocation parameters, and geophysical corrections for each laser shot for all eight GEDI beams. GEDI01_B data are created by geolocating the GEDI01_A raw waveform data. The GEDI01_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.    The GEDI01_B data product contains 85 layers for each of the eight beams including the geolocated corrected and smoothed waveform datasets and parameters and the accompanying ancillary, geolocation, and geophysical correction. Additional information can be found in the GEDI L1B Product Data Dictionary.",
         "license": "proprietary"
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         "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.    The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.    The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.  ",
         "license": "proprietary"
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         "description": "The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth\u2019s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6\u00b0 N and 51.6\u00b0 S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.    The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.    The GEDI02_B data product contains 96 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) GEOLST4KHR version 2 swath product provides per-pixel Land Surface Temperature (LST) with a spatial resolution of 4,000 meters (m). The product is produced daily in hourly increments using data acquired from Geostationary Operational Environmental Satellite (GOES) 8 and 10 through 15 satellites for the years 2000\u20132017. The GEOLST4KHR product provides LST values for both North and South America. The GEOLST4KHR data product utilizes the Modern-Era Retrospective analysis for Research and Applications Version 2 / Radiative Transfer for TIROS Operational Vertical Sounder (MERRA-2/RTTOV) Single-Channel Emissivity-Combined ASTER and MODIS Emissivity over Land (CAMEL) algorithm.    The GEOLST4KHR product provides layers for cloud mask, latitude, longitude, land surface temperature, and land surface temperature error. A low-resolution browse is also available showing land surface temperature as an RGB (red, green, blue) image in JPEG format.  ",
         "license": "proprietary"
     },
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003) and Forest Cover Change (GFCC30FCC) (http://dx.doi.org/10.5067/MEaSUREs/GFCC/GFCC30FCC.001) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme.  Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). ",
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. The accuracy assessment and validation process utilizes a cluster of 3 by 3 pixels of 30 meter data to resample the product to 90 meter resolution. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe.",
         "license": "proprietary"
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         "description": "The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy.    The GHISA for Central Asia (GHISACASIA) Version 1 product provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia. The GHISA hyperspectral library for the two irrigated areas was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired in 2007 and ASD (Analytical Spectral Devices, Inc.) Spectroradiometer data acquired in 2006 and 2007. GHISACASIA is extracted from three Hyperion hyperspectral images and several thousands of field ASD Spectroradiometer data. Measurements were taken from 1,232 randomly chosen points scattered across the two farm sites throughout the growing season. All the processing algorithms are coded in Statistical Analysis System (SAS) format and available for download.    Provided in the .xlsx files are the spectral library including image information, plot IDs, study area, instrument, Julian or acquisition date, and crop type labels for Central Asia sample fields.  ",
         "license": "proprietary"
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         "license": "proprietary"
     },
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         "title": "GLAS/ICESat L1A Global Altimetry Data (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
+        "catalog": "NSIDC_CPRD STAC Catalog",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.html",
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         "license": "proprietary"
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         "id": "GLAH01_033",
         "title": "GLAS/ICESat L1A Global Altimetry Data (HDF5) V033",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.html",
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         "license": "proprietary"
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         "id": "GLAH02_033",
         "title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.html",
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         "license": "proprietary"
     },
     {
         "id": "GLAH02_033",
         "title": "GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033",
-        "catalog": "NSIDC_CPRD STAC Catalog",
+        "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.html",
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         "description": "GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.",
         "license": "proprietary"
     },
@@ -80092,19 +79806,6 @@
         "description": "Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.",
         "license": "proprietary"
     },
-    {
-        "id": "GLAH04_033",
-        "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
-        "state_date": "2003-02-20",
-        "end_date": "2009-10-11",
-        "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html",
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-        "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
-        "license": "proprietary"
-    },
     {
         "id": "GLAH04_033",
         "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
@@ -80119,16 +79820,16 @@
         "license": "proprietary"
     },
     {
-        "id": "GLAH05_034",
-        "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034",
+        "id": "GLAH04_033",
+        "title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
         "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html",
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-        "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization).  Each data granule has an associated browse product.",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIiwidW1tIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIn0%3D/GLAH04_033",
+        "description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
         "license": "proprietary"
     },
     {
@@ -80145,16 +79846,16 @@
         "license": "proprietary"
     },
     {
-        "id": "GLAH06_034",
-        "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034",
+        "id": "GLAH05_034",
+        "title": "GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034",
         "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIiwidW1tIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIn0%3D/GLAH06_034",
-        "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIiwidW1tIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIn0%3D/GLAH05_034",
+        "description": "GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization).  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
@@ -80171,16 +79872,16 @@
         "license": "proprietary"
     },
     {
-        "id": "GLAH07_033",
-        "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
+        "id": "GLAH06_034",
+        "title": "GLAS/ICESat L1B Global Elevation Data (HDF5) V034",
         "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIiwidW1tIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIn0%3D/GLAH07_033",
-        "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIiwidW1tIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIn0%3D/GLAH06_034",
+        "description": "GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
@@ -80196,6 +79897,19 @@
         "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
+    {
+        "id": "GLAH07_033",
+        "title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
+        "catalog": "NSIDC_ECS STAC Catalog",
+        "state_date": "2003-02-20",
+        "end_date": "2009-10-11",
+        "bbox": "-180, -86, 180, 86",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIiwidW1tIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIHdlZWtseSBncmlkZGVkIGF0bW9zcGhlcmUgdjAwNVwiLFwiTlNJRENfRUNTXCIsXCJBVEwxNlwiLFwiNVwiLDI3Mzc5OTcyNDMsNjRdIn0%3D/GLAH07_033",
+        "description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.",
+        "license": "proprietary"
+    },
     {
         "id": "GLAH08_033",
         "title": "GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033",
@@ -80225,78 +79939,78 @@
     {
         "id": "GLAH09_033",
         "title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
+        "catalog": "NSIDC_CPRD STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIiwidW1tIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIn0%3D/GLAH09_033",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDE2XSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxNl0ifQ%3D%3D/GLAH09_033",
         "description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
         "id": "GLAH09_033",
         "title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033",
-        "catalog": "NSIDC_CPRD STAC Catalog",
+        "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDE2XSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxNl0ifQ%3D%3D/GLAH09_033",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIiwidW1tIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIn0%3D/GLAH09_033",
         "description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
         "id": "GLAH10_033",
         "title": "GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
+        "catalog": "NSIDC_CPRD STAC Catalog",
         "state_date": "2003-09-25",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.html",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDE2XSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxNl0ifQ%3D%3D/GLAH10_033",
         "description": "GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds.  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
         "id": "GLAH10_033",
         "title": "GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033",
-        "catalog": "NSIDC_CPRD STAC Catalog",
+        "catalog": "NSIDC_ECS STAC Catalog",
         "state_date": "2003-09-25",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections?cursor=eyJqc29uIjoiW1wiYXRsYXMvaWNlc2F0LTIgbDNiIG1vbnRobHkgZ3JpZGRlZCBkeW5hbWljIG9jZWFuIHRvcG9ncmFwaHkgdjAwM1wiLFwiTlNJRENfQ1BSRFwiLFwiQVRMMTlcIixcIjNcIiwyNzU0OTU2Nzg2LDE2XSIsInVtbSI6IltcImF0bGFzL2ljZXNhdC0yIGwzYiBtb250aGx5IGdyaWRkZWQgZHluYW1pYyBvY2VhbiB0b3BvZ3JhcGh5IHYwMDNcIixcIk5TSURDX0NQUkRcIixcIkFUTDE5XCIsXCIzXCIsMjc1NDk1Njc4NiwxNl0ifQ%3D%3D/GLAH10_033",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIiwidW1tIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIn0%3D/GLAH10_033",
         "description": "GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds.  Each data granule has an associated browse product.",
         "license": "proprietary"
     },
     {
         "id": "GLAH11_033",
         "title": "GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033",
-        "catalog": "NSIDC_ECS STAC Catalog",
+        "catalog": "NSIDC_CPRD STAC Catalog",
         "state_date": "2003-02-20",
         "end_date": "2009-10-11",
         "bbox": "-180, -86, 180, 86",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIiwidW1tIjoiW1wiZ2xhcy9pY2VzYXQgbDFiIGdsb2JhbCBlbGV2YXRpb24gZGF0YSAoaGRmNSkgdjAzNFwiLFwiTlNJRENfRUNTXCIsXCJHTEFIMDZcIixcIjM0XCIsMTAwMDAwMDQ0NSwxNjhdIn0%3D/GLAH11_033",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.html",
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         "license": "proprietary"
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         "title": "GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033",
-        "catalog": "NSIDC_CPRD STAC Catalog",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.html",
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         "description": "GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.",
         "license": "proprietary"
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         "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034",
-        "catalog": "NSIDC_ECS STAC Catalog",
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+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.html",
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         "id": "GLAH13_034",
         "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034",
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         "id": "GLAH14_034",
         "title": "GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034",
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         "id": "GLAH14_034",
         "title": "GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034",
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         "license": "proprietary"
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         "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.    The purpose of G-LiHT\u2019s Canopy Height Model data product (GLCHMT) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA\u2019s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.    GLCHMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.   ",
         "license": "proprietary"
     },
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "Goddard\u2019s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.  The purpose of G-LiHT\u2019s Trajectory data product (GLTRAJECTORY) is to provide aircraft location and orientation to support and supplement other G-LiHT data products.  GLTRAJECTORY data are processed as a Google Earth overlay Keyhole Markup Language (KML) file over the extent of an entire flight path. A low resolution browse is also provided to show the flight path.  ",
         "license": "proprietary"
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         "description": "NASA\u2019s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, the North American, South American, and European continents are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management.     The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.  ",
         "license": "proprietary"
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         "description": "The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners.  In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating.  Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8.  The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.",
         "license": "proprietary"
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         "description": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite.   The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo- France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral algorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible in daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 30 minutes slot is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating 30 minute SST data available in one hour time, and the priority being given to the value the closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
         "license": "proprietary"
     },
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         "description": "The  Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision.  For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset,  skin sea surface temperature (SST) measurements  are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including  Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.  The full disk image is subsetted into granules representing  distinct northern and southern regions.",
         "license": "proprietary"
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         "description": "The  Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision.  For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset,  skin sea surface temperature (SST) measurements  are calculated from the far IR channels of GOES-15 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including  Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.  The full disk image is subsetted into granules representing  distinct northern and southern regions.",
         "license": "proprietary"
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         "description": "The data is regional and part of the Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset covering the America Region based on retrievals from the Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-16 (GOES-16). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from GOES-16 in the Eastern position. GOES-16 Imager level 1 data are acquired at M\u00e9t\u00e9o-France/Centre de M\u00e9t\u00e9orologie Spatiale (CMS) through the EUMETSAT/EUMETCast system. The GOES-16 ABI enables daytime SST calculations (whereas, previously, GOES East SST was restricted to nighttime conditions). The L3C SST is derived from a three-band (centered at 8.4, 10.3, and 12.3 um) algorithm. The ABI split-window configuration features three bands instead of the two found in heritage sensors (GOES-13). The 8.5-um is used in conjunction with the 10.3-um and 12.3-um bands for improved thin cirrus detection as well as for better atmospheric moisture correction in relatively dry atmospheres. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Each 10-minute observation interval is processed at full satellite resolution. The operational products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 135W-15W) SST fields obtained by aggregating the available10-minute SST data into hourly files-hour time, with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882111-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882111-POCLOUD.html",
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         "description": "FOR EXPERT USE ONLY. The GRACE-FO Level-1A data contains telemetry data that has been converted to engineering units, from which Level-1B data products are derived. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882118-POCLOUD.html",
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         "description": "FOR EXPERT USE ONLY. The GRACE-FO Level-1B data provide all necessary inputs to derive monthly time variations in the Earth gravity field. Level-1B data are also used for GRACE orbit and mean gravity field determination. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).",
         "license": "proprietary"
     },
-    {
-        "id": "GRACEFO_L2_CSR_MONTHLY_0062_6.2",
-        "title": "GRACE-FO Level-2  Monthly Geopotential Spherical Harmonics CSR Release 06.2 (RL06.2)",
-        "catalog": "POCLOUD STAC Catalog",
-        "state_date": "2018-05-22",
-        "end_date": "",
-        "bbox": "-180, -89, 180, 89",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2753948997-POCLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2753948997-POCLOUD.html",
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-        "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas at Austin Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.2 data is an updated version of the initial GRACE-FO RL06 and RL06.1 Level-2 data products. RL06.2 differs from RL06.1 only in the handling of GPS data set during Level-2 processing. GPS handling for RL06.2 was re-assessed due to steady degradation in low degrees during late 2022 and 2023 in RL06.1. Alterations in the processing of the GPS data were made to increase the volume of data available after editing. Additionally relative weighting between KBR and GPS was updated after June 2020 to use optimal weighting instead of capped weights for GPS used for RL06.1 solutions. The resulting RL06.2 time series shows improvement in the low degree coefficients after 2022. All GRACE-FO RL06.2 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields. Refer to the mission page for more information.",
-        "license": "proprietary"
-    },
     {
         "id": "GRACEFO_L2_CSR_MONTHLY_0063_6.3",
         "title": "GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics CSR Release 06.3 (RL06.3)",
@@ -84807,23 +84508,10 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3059656222-POCLOUD.html",
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         "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas (at Austin) Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.",
         "license": "proprietary"
     },
-    {
-        "id": "GRACEFO_L2_GFZ_MONTHLY_0061_6.1",
-        "title": "GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics GFZ Release 6.1 (RL06.1)",
-        "catalog": "POCLOUD STAC Catalog",
-        "state_date": "2018-05-22",
-        "end_date": "",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2257298799-POCLOUD.umm_json",
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-        "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.1 data is an updated version of the initial GRACE-FO RL06 Level-2 data products. RL06.1 differs from RL06 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.1 uses ACH1B RL04, which replaces ACT1B RL04 that was used for Level-2 RL06. All GRACE-FO RL06.1 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields.  Refer to the mission page for more information. ",
-        "license": "proprietary"
-    },
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         "id": "GRACEFO_L2_GFZ_MONTHLY_0063_6.3",
         "title": "GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics GFZ Release 6.3 (RL06.3)",
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         "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. Refer to the mission page for more information.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-deadband operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3252991748-POCLOUD.html",
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         "description": "This dataset provides the first year of hourly ocean bottom pressure measurements at the North Pole from the Arctic Bottom Pressure Recorder - Follow On (ABPR-FO) deployed in August 2022. Based around a Paroscientific Digiquartz pressure sensor, the ABPR-FO is designed and powered to collect and transmit data for 5 years. The 5-year record will be collected, quality-controlled and updated at PO.DAAC on a yearly basis. The full time series is provided as a single netCDF file, and reports both bottom pressure (reported as liquid water equivalent thickness) and temperature. This dataset aims to serve as in situ validation for GRACE-FO data products in the central Arctic. This project is funded by NASA\u2019s GRACE and GRACE-FO Science Team and supported by PONANT Science.   ",
         "license": "proprietary"
     },
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         "description": "The GRACE Atmosphere and Ocean De-aliasing dataset contains spherical harmonic coefficients of combined barotropic or baroclinic sea level and vertical integrated pressure variations at 6-hour sample rate.  It is used as a correction product for the Level 2 GRACE datasets.",
         "license": "proprietary"
     },
@@ -84885,7 +84573,7 @@
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of geopotential field derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements and a non-tidal atmospheric model produced by the German Research Centre for Geosciences (GFZ).  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
     },
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ).  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of ocean bottom pressure derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL).  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the Center for Space Research (CSR) at University of Texas at Austin.  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the German Research Centre for Geosciences (GFZ).  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  This dataset contains estimates of static field geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment (GRACE) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL).  The data are in spherical harmonics averaged over approximately a month.  The primary objective of the GRACE mission is to obtain accurate estimates of the mean and time-variable components of the gravity field variations.  This objective is achieved by making continuous measurements of the change in distance between twin spacecraft, co-orbiting in about 500 km altitude, near circular, polar orbit, spaced approximately 200 km apart, using a microwave ranging system.  In addition to these range change, the non-gravitional forces are measured on each satellite using a high accuracy electrostatic, room-temperature accelerometer.  The satellite orientation and position (and timing) are precisely measured using twin star cameras and a GPS receiver, respectively.  Spatial and temporal variations in the gravity field affect the orbits (or trajectories) of the twin spacecraft differently.  These differences are manifested as changes in the distance between the spacecraft, as they orbit the Earth.  This change in distance is reflected in the time-of-flight of microwave signals transmitted and received nearly simultaneously between the two spacecraft.  The change in this time of fight is continuously measured by tracking the phase of the microwave carrier signals.  The so called dual-one-way range change measurements can be reconstructed from these phase measurements.  This range change (or its numerically derived derivatives), along with other mission and ancillary data, is subsequently analyzed to extract the parameters of an Earth gravity field model.",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field.  Level 1B data are also used for GRACE orbit and mean gravity field determination.  It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)",
         "license": "proprietary"
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         "description": "FOR EXPERT USE ONLY.  The GRACE Level 1B data provide all necessary inputs to derive monthly time variations in the Earth's gravity field.  Level 1B data are also used for GRACE orbit and mean gravity field determination.  It contains K-Band Ranging Data Product (KBR1B), Star Camera Data Product (SCA1B), Accelerometer Data Product (ACC1B), GPS Data Product (GPS1B), Vector Products (VGN1B, VGO1B, VGB1B, VCM1B, VKB1B, VSL1B), Quaternion Products (QSA1B, QSB1B), and Housekeeping Products (AHK1B, IHK1B, THR1B, TNK1B, MAG1B, MAS1B, TIM1B)The GRACE Level-1B RL03 data consists only of updated spacecraft attitude (SCA1B) and K-band inter-satellite ranging (KBR1B) data. All other Level-1B were not changed and it is recommended to use the RL02 products with the updated RL03 KBR1B and SCA1B products. The RL03 SCA1B data were corrected for a stellar aberration error in the onboard star tracker software and incorrect data weighting in the star tracker combination software. For the RL03 SCA1B data a new software module was developed that uses Kalman filtering, field of view error modeling, relative alignment adjustment and the inclusion of angular spacecraft body acceleration measurements from the ACC instrument. This new processing resulted in a significant reduction in high frequency noise and the elimination of jumps during transitions between dual and single star tracker operation.  The KBR1B product is updated as well because the KBR antenna phase center range correction, range rate correction and range acceleration are computed using the spacecraft attitude information (SCA1B). Only these three correction values were updated in the KBR1B product. All other entries in the KBR1B remained the same.",
         "license": "proprietary"
     },
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         "license": "proprietary"
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         "description": "GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model. For further details, please refer to https://www.gfz-potsdam.de/en/aod1b/.    The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models).",
         "license": "proprietary"
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         "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.1Mv03 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.    The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.    Provided in the GWELDMO product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3.1 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 1985, 1990, and 2000 epochs. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.    The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.    Provided in the GWELDMO product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.    Version 3.1 products use Landsat Collection 1 products as input and have improved per-pixel cloud mask, new quality data, improved calibration information, and improved product metadata that enable view and solar geometry calculations.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Monthly (GWELDMO) Version 3.2 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over monthly reporting periods for the 2005 epoch. GWELD products are generated from all available Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provides a consistent data source to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.    The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid.    Provided in the GWELDMO product are layers for surface reflectance bands 1-5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.    GWELD Version 3.2 products now use Landsat Collection 2 products as input while previous GWELD versions use Landsat Collection 1. Additionally, the Landsat FMask layer, CFMask_State, was adopted as the cloud mask replacing the DT_Cloud_State and ACCA_State layers.",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Web-Enabled Landsat Data Annual (GWELDYR) Version 3 data product provides Landsat data at 30 meter (m) resolution for terrestrial non-Antarctica locations over annual reporting periods for the 2010 epoch. GWELD data products are generated from all available Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data in the U.S. Geological Survey (USGS) Landsat archive. The GWELD suite of products provide consistent data to derive land cover as well as geophysical and biophysical information for regional assessment of land surface dynamics.    The GWELD products include Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) for the reflective wavelength bands and to top of atmosphere (TOA) brightness temperature for the thermal bands. The products are defined in the Sinusoidal coordinate system to promote continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) land tile grid    Provided in the GWELDYR product are layers for surface reflectance bands 1 through 5 and 7, TOA brightness temperature for thermal bands, Normalized Difference Vegetation Index (NDVI), day of year, ancillary angle, and data quality information. A low-resolution red, green, blue (RGB) browse image of bands 5, 4, 3 is also available for each granule.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The H09-AHI-L2P-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.  <br><br>  The H08/AHI maps SST in a Full Disk (FD) area from 80E-160W and 60S-60N, with spatial resolution 2km at nadir to 15km/VZA (view zenith angle) 67-deg, and 10-min temporal sampling. The 10-min FD data are subsequently collated in time, to produce the 1-hr product, with improved coverage and reduced cloud leakages and image noise. The L2P data is produced in GHRSST compliant netCDF4 GDS2 format, with 24 granules per day, and a total data volume 1.2 GB/day. The near-real time (NRT) data are updated hourly, with several hours latency. The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).  <br><br>  Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Pixel locations  can be obtained using a flat lat/lon file or a Python script available via Documents tab from the dataset landing page. Climate and Forecast (CF) metadata aware software (e.g., Panoply, xarray) can detect and map the data as is via the granule CF projection attributes and variables. The ACSPO H09 HAI SSTs are validated against quality controlled in situ data from the NOAA iQuam system (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). A 0.02-deg equal-angle gridded L3C product 0.7GB/day) is available at https://podaac.jpl.nasa.gov/dataset/H09-AHI-L3C-ACSPO-v2.90  ",
         "license": "proprietary"
     },
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         "description": "The H09-AHI-L3C-ACSPO-v2.90 dataset contains the Subskin Sea Surface Temperature (SST) produced  by the NOAA ACSPO system from the Advanced Himawari Imager (AHI; largely identical to GOES-R/ABI) onboard the Himawari-9 (H09) satellite. The H09 is a Japanese weather satellite, the 9th of the Himawari geostationary weather satellite operated by the Japan Meteorological Agency. It was launched on November 2, 2016 into its nominal position at 140.7-deg E, and declared operational on December 13, 2022, replacing the Himawari-8. The AHI is the primary instrument on the Himawari Series for imaging Earth\u2019s weather, oceans, and environment with high temporal and spatial resolutions.  <br><br>  The H09-AHI-L3C-ACSPO-v2.90 dataset is a gridded version of the ACSPO H09-AHI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/AHI_H09-STAR-L2P-v2.90).  The L3C (Level 3 Collated) data is mapped on 0.02-deg lat-lon grid and outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.  <br><br>  The ACSPO H09/AHI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of approximately 2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA for DM instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).    ",
         "license": "proprietary"
     },
@@ -86956,19 +86644,6 @@
         "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).",
         "license": "proprietary"
     },
-    {
-        "id": "HAWKEYE_L1_1",
-        "title": "SeaHawk-1 HawkEye Level-1A  Data, version 1",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "2018-12-03",
-        "end_date": "2023-10-27",
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         "id": "HAWKEYE_L2_OC_2018.0",
         "title": "SeaHawk HawkEye Regional Ocean Color (OC) Data, version 2018.0",
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         "description": "The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).",
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-        "title": "SeaHawk-1 HawkEye Level-2 Regional Ocean Color (OC) Data, version 2022.0",
-        "catalog": "OB_CLOUD STAC Catalog",
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         "title": "Monthly Climate Data for Selected USGS HCDN Sites, 1951-1990, R1",
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         "description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.    The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS)(https://hls.gsfc.nasa.gov/products-description/tiling-system/) tiling system, and thus are \u201cstackable\u201d for time series analysis.    The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.",
         "license": "proprietary"
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         "description": "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe\u2019s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2\u20133 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.     The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) (https://hls.gsfc.nasa.gov/products-description/tiling-system/) tiling system, and thus are \u201cstackable\u201d for time series analysis.    The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.  ",
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2263336836-POCLOUD.html",
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         "description": "This data set contains the monthly Global Ocean Mass Anomalies (goma) since 04/2002, as measured by the GRACE and GRACE Follow-On (G/GFO) satellite missions. The data are averaged over the global ocean domain, at monthly intervals (note: data gaps exist). This file contains the goma time series based on the spherical harmonic gravity fields provided by the G/GFO SDS centers: JPL, CSR, GFZ. The data are frequently updated as new monthly observations are acquired by the GFO mission. The processing of the spherical harmonics gravity field coefficients is as follows: (1) GAD + GSM: the monthly de-aliasing product GAD is added back to the GSM L2 gravity fields; (2) [GSM + GAD] coefficients are averaged over the global ocean with a coastal buffer of 300 km (to avoid land-ocean leakage); (3) the spatial mean of atmospheric loading of the entire global ocean domain is removed (via the GAA L2 data product). A GIA correction using the ICE-6GD model (Peltier et al., 2018) is applied.",
         "license": "proprietary"
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         "description": "The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO);   Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09.   The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution\u2019s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.",
         "license": "proprietary"
     },
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         "description": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-A (MetOp-A&#65289;satellite &#65288;launched 19 Oct 2006). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.",
         "license": "proprietary"
     },
@@ -88733,7 +88395,7 @@
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Infrared Atmospheric Sounding Interferometer (IASI) on the European Meteorological Operational-B (MetOp-B)satellite (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from METOP/IASI. The Infrared Atmospheric Sounding Interferometer (IASI) measures inthe infrared part of the electromagnetic spectrum at a horizontal resolution of 12 km at nadir up to40km over a swath width of about 2,200 km. With 14 orbits in a sun-synchronous mid-morningorbit (9:30 Local Solar Time equator crossing, descending node) global observations can beprovided twice a day. The SST retrieval is performed and provided by the IASI L2 processor atEUMETSAT headquarters. The product format is compliant with the GHRSST Data Specification(GDS) version 2.",
         "license": "proprietary"
     },
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         "description": "The enhanced Jason-1 Microwave Radiometer (JMR) corrections contains better wet tropospheric path delay corrections along with better land, rain and ice flagging for coastal regions than that found in the Jason-1 Geophysical Data Records (GDR).  The enhanced corrections can be used in place of the GDR wet troposphere correction to provide more accurate Sea Surface Height Anomalies for coastal regions.",
         "license": "proprietary"
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         "description": "The Jason-1 Geophysical Data Records (GDR) contain full accuracy altimeter data to measure sea surface height, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height. Sea surface height anomalies calculation and recommended data edit criteria are specified in the Jason-1 GDR User Handbook at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/jason1/open/L2/gdr_netcdf_e/docs/Handbook_Jason-1_v5.1_April2016.pdf",
         "license": "proprietary"
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         "description": "The Jason-1 Geophysical Data Records (GDR) Geodetic Mission contain full accuracy altimeter data, with a high precision orbit, provided approximately 35 days after data collection. The data are sorted into cycles that are approximately 11 days long and contain 280 pass files.  The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The GDR contain all relevant corrections needed to calculate the sea surface height.",
         "license": "proprietary"
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         "description": "These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR). Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation.  Using the various parameter the SSHA can be calculated and are provided in this dataset.  The data are in NetCDF format. This dataset only contains the parameters that are directly related to SSHA.",
         "license": "proprietary"
     },
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         "description": "These Sea Surface Height Anomalies (SSHA) are derived from the Jason-1 Geophysical Data Record (GDR) Geodetic Mission. Jason-1 is an altimetric mission whose instruments make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, and position relative to the GPS satellite constellation.  Using the various parameter the SSHA can be calculated and are provided in this dataset.  The data are in NetCDF format.",
         "license": "proprietary"
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         "description": "The Sensory Geophysical Data Record (SGDR) files contain full accuracy altimeter data, with a high precision orbit (accuracy ~1.5 cm). The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking.  The SGDR is an expert level product, if you do not require the waveforms then the GDR/GPN or GPR will be more suited for your needs.",
         "license": "proprietary"
     },
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         "description": "The Sensory Geophysical Data Record (SGDR) files from the Geodetic Mission contain full accuracy altimeter data, with a high precision orbit. The instruments on Jason-1 make direct observations of the following quantities: altimeter range, significant wave height, ocean radar backscatter cross-section (a measure of wind speed), ionospheric electron content (derived by a simple formula), tropospheric water content, mean sea surface, and position relative to the GPS satellite constellation. The SGDR contain all relevant corrections needed to calculate the sea surface height. It also contains the 20Hz waveforms that are required for retracking.  The SGDR is an expert level product, if you do not require the waveforms then the GDR will be more suited for your needs.",
         "license": "proprietary"
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         "description": "This is a near real time dataset that provides a GPS based orbit and Sea Surface Height Anomalies (SSHA) from that orbit. It is similar to the Jason-3 Operation Geophysical Data Record (OGDR) that is distributed at NOAA (http://www.nodc.noaa.gov/sog/jason/), but includes the GPS orbit and SSHA as two additional variables. It has a 5 hour time lag due to the time needed to calculate the GPS orbit and SSHA.  The GPS orbits have been shown to be more accurate than the DORIS orbits on a near real time scale and therefore produces a more accurate SSHA.",
         "license": "proprietary"
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         "description": "This dataset provides supplementary wet tropospheric corrections for historical Jason-3 observations (https://www.ncei.noaa.gov/archive/accession/Jason3-xGDR). Recent assessments of the global sea level budget have resulted in increased scrutiny of estimates of global sea level change based on Jason-3. After a careful assessment of the wet tropospheric correction derived from the Advanced Microwave Radiometer (AMR) instrument, it was determined that further improvements to the accuracy of the historical Jason-3 observations could be made. Since this assessment was only completed after Jason-3 data was reprocessed to GDR-F (Geophysical Data Record \u2013 Version F) standards, it was not included in the GDR-F product release. For this reason, this supplementary correction product has been created using the method of Brown et al. (2012) to allow users to correct path delay and sea surface height observations, reducing errors in estimates of global sea level change by 2-3 mm over 8 years.<br><br>  The correction was computed based on comparison of the AMR-observed brightness temperatures with independent satellite observations from the Special Sensor Microwave Imager Sounder (SSMI), F16, F17 and F18, Fundamental Climate Data Records.  SSMI data was obtained from the NOAA Climate Data Record (CDR) of SSMIS Microwave Brightness Temperatures, RSS Version 8 (Wentz et al., 2019, https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C01567/html). The method described in Brown et al. (2012) to map SSMI Brightness Temperatures to AMR equivalent brightness temperatures (TBs) was used. Although it was found that it made little difference to the result, a bias was removed between SSMI equivalent AMR TBs and AMR TBs with respect to latitude for all data prior to computing temporal trends.  In addition, only rain free, mostly clear data (TB18.7 GHz < 160K) data were considered.<br><br>  The correction is supplied on a pass-by-pass basis in a 4-column text file. See the product documentation for guidance on how to apply it to Jason-3 observations.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset contains reconstructed global-mean sea level evolution and the estimated contributing processes over 1900-2018. Reconstructed sea level is based on annual-mean tide-gauge observations and uses the virtual-station method to aggregate the individual observations into a global estimate. The contributing processes consist of thermosteric changes, glacier mass changes, mass changes of the Greenland and Antarctic Ice Sheet, and terrestrial water storage changes. The glacier, ice sheet, and terrestrial water storage are estimated by combining GRACE observations (2003-2018) with long-term estimates from in-situ observations and models. Steric estimates are based on in-situ temperature profiles. The upper- and lower bound represent the 5 and 95 percent confidence level. The numbers are equal to the ones presented in Frederikse et al. The causes of sea-level rise since 1900, Nature, 2020.This dataset was produced by the Heat and Ocean Mass from Gravity ESDR (HOMAGE) project, with funding from MeASUREs-2017. HOMAGE is combining satellite observations to create a set of ESDRs that provide a homogeneous basis for accurate and current quantification of the planetary sea level budget, ocean heat content, and large-scale ocean transport variations.",
         "license": "proprietary"
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         "description": "This is a Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis dataset produced daily on an operational basis by the Naval Oceanographic Office (NAVO) on a global 0.1x0.1 degree grid. The K10 (NAVO 10-km gridded SST analyzed product) L4 analysis uses SST observations from the following instruments: Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The AVHRR data for this comes from the MetOp-A, MetOp-B, and NOAA-19 satellites; VIIRS data is sourced from the Suomi_NPP satellite; SEVIRI data comes from the Meteosat-8 and -11 satellites. The age (time-lag), reliability, and resolution of the data are used in the weighted average with the analysis tuned to represent SST at a reference depth of 1-meter. Input data from the AVHRR Pathfinder 9km climatology dataset (1985-1999) is used when no new satellite SST retrievals are available after 34 days. Comparing with its predecessor (DOI: https://doi.org/10.5067/GHK10-L4N01 ), this updated dataset has no major changes in Level-4 interpolated K10 algorithm, except for using different satellite instrument data, and updating metadata and file format. The major updates include: (a) updated and enhanced the granule-level metadata information, (b) converted the SST file from GHRSST Data Specification (GDS) v1.0 to v2.0, (c) added the sea_ice_fraction variable to the product, and (d) updated the filename convention to reflect compliance with GDS v2.0.",
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         "description": "NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format.  The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs.",
         "license": "proprietary"
     },
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         "description": "The L3S_LEO_DY-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from multiple instruments, including the VIIRS onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites, AVHRR onboard Metop-A, B , C satellites and MODIS onboard the Terra and Aqua satellites. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product. <br><br>     The L3S-DY-SST combines the both L3S-LEO-PM/AM SSTs into a single daily product. It covers from 2000-02-24 to present and is reported in one file per 24h interval. Data are in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 succeeds the v2.80 dataset (not available from the  PO.DAAC) with the following improvements: (1) The L3S-LEO-PM input was updated from v2.80 to v2.81; and (2) ACSPO Terra MODIS SST is included from 2000-02-24 to 2021-12-31. The inclusion of Terra extends the availability of L3S-LEO-DY back to 2000-02-24 (from 2006-12-01 in v2.80).  The SST diurnal warming effects from different daily observation times across the series of instruments have been corrected and are described in the publications by Jonasson et al., 2022 <br><br>    The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names.  They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam) ",
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         "description": "The L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite.  The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02\u00ba resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO-PM ( https://doi.org/10.5067/GHLPM-3S281 ) and AM ( https://doi.org/10.5067/GHLAM-3SS28 ) data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product ( https://doi.org/10.5067/GHLDY-3S281 ). <br><br>    This PM SST product is derived by collating individual satellite ACSPO L3U data ( https://doi.org/10.5067/GHVRS-3UO61, https://doi.org/10.5067/GHV20-3UO61 and https://doi.org/10.5067/GHN21-3U280 ). It covers from 2002-07-04 to present and is reported in 2 files daily, day and night, at 1:30am/pm local time. The SST is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 is updated from the previous v2.80 ( https://doi.org/10.5067/GHLPM-3SS28 ): (1) v2.81 includes 3 VIIRSs (NPP, N20, and N21 from 2023-03-19 - on); (2) Aqua MODIS SST included from 2002-07-04 to 2022-12-31; (3) Time series in v2.81 extended back to 2002-07-04 (from 2012-02-01 in v2.80); (4) recently uncovered VIIRS daytime SST drifts in NPP and N20 SSTs of approximately -0.1 K/decade mitigated. <br><br>    The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names.  They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam; https://www.star.nesdis.noaa.gov/socd/sst/iquam), and monitored in another NOAA system, SST Quality Monitor (SQUAM; https://www.star.nesdis.noaa.gov/socd/sst/squam)  ",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 1 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present. The MEaSUREs LEOLST product is generated by regridding the monthly LST CMG products from MODIS (MYD21C3.061) and VIIRS (VNP21C3.002).     The product will be available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format.  ",
         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 2 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present.The MEaSUREs LEOLST product is generated by regridding the monthly CMG products from Aqua MODIS (MYD21C3) and VIIRS (VNP21C3 and VJ121). The product is available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format. ",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2592845930-LPDAAC_ECS.html",
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         "description": "The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, global cropland data to assist and address food and water security issues of the twenty-first century.  As an extension of the Global Food Security-support Analysis Data (GFSAD) project, LGRIP maps the world\u2019s agricultural lands by dividing them into irrigated and rainfed croplands, and calculates irrigated and rainfed areas for every country in the world. LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.    Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10\u00b0 by 10\u00b0 area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.   ",
         "license": "proprietary"
     },
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         "description": "This dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings. LOCSS project aims to collaborate with local citizens to monitor small and medium sized lakes (i.e., lakes with an average surface area less than 100 km2). At each location, a lake gauge is installed and provided with a cellphone number. Local citizens read the water level at each lake gauge and sent it in a text message. Data can also be manually collected and uploaded later from the website in remote places where cellphone signal is challenged. The readings are specified in cm, m, or ft, according to the local unit system. This version of the dataset has lakes located in seven (7) countries: Bangladesh, India, Canada, the United States, Pakistan, and Nepal. This product consists of two files in comma-separated values (csv) format : 1) the list of gauges whose attributes include gauge coordinates, installation dates, the height of the gauge, reading units, city, time zone, and installation notes; 2) list of readings by each gauge specified in the local time. To discover more details about LOCSS, please visit https://www.locss.org/.",
         "license": "proprietary"
     },
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         "description": "LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet South America contains data across South America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice. <br>There are a total of 1200 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files): * Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution * Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution * Landsat-8  surface reflectance product from Collection 2 Level-2 <br>Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/).",
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         "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 data product provides a spatially aggregated and reprojected version of the tiled MCD12Q1 Version 6.1 (https://doi.org/10.5067/MODIS/MCD12Q1.061) data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe from 2001 to 2021. Additionally, sub-pixel proportions of each land cover class in each 0.05 degree pixel is provided along with the aggregated quality assessment information for each of the three land classification schemes.   Provided in each MCD12C1 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3 Assessment, and Majority Land Cover Type 1-3 Percent.  Validation at stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS land cover products.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD12C1 Version 6.1 product has a minor fix to UMD Land Cover Class.",
         "license": "proprietary"
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         "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals (2001-2022). The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology.     Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6.1 Hierarchical Data Format 4 (HDF4) file.    Validation at stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS land cover products.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The MCD12Q1 Version 6.1 product has a minor fix to UMD Land Cover Class.",
         "license": "proprietary"
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         "description": "The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals from 2001 to 2021. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes.  Provided in each MCD12Q2 Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. SDS layers may be multi-dimensional with up to two valid vegetation cycles.   For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year.  Validation accuracy (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) is currently still being evaluated, and a validation statement for this product will be forthcoming once the evaluation is complete.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD12Q2 Version 6.1 product has an improved approach to snow filtering.   ",
         "license": "proprietary"
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         "description": "The MCD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA\u2019s Terra and Aqua satellites from within the 8-day period.    LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.    The LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.    Improvements/Changes from Previous Versions     * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
         "license": "proprietary"
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         "description": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA\u2019s Terra and Aqua satellites from within the 4-day period.  LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.  The LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC)  algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption.  The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits.  Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer.  Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR.  * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. ",
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         "description": "The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products (MCD19A1CMGL (https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and MCD19A1CMGO (https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)).     The MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 \u00b5m, green band AOD at 0.55 \u00b5m, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 \u00b5m created using a composite of all available orbits. ",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The MCD19A3D Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product. Output daily at 1 kilometer (km) resolution, the Multi-angle Implementation of Atmospheric Correction (MAIAC) MCD19A3D product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions.  When snow is detected, gap-filled snow grain size and sub-pixel snow fraction are computed. The gap-filling process retains the parameter in MAIAC\u2019s memory for each grid cell until updated with the latest cloud-free observation. The number of days since the last update is provided in a separate layer.  Over snow-free land, MAIAC also reports gap-filled Normalized Difference Vegetation Index (NDVI) at 1 km resolution and gap-filled Nadir BRDF-Adjusted Reflectance (NBAR) at 250 m resolution in the red and near-infrared (NIR) bands.  The MCD19A3 BRDF Model Parameters product contains the following Science Dataset (SDS) layers: RTLS isotropic kernel parameter (Kiso) for bands 1-8, the RTLS volumetric kernel parameter (Kvol) for bands 1-8, RTLS geometric kernel parameter (Kgeo) for bands 1-8, three snow parameters, NDVI, NBAR, and three separate layers for the number of days since last update to current day.   Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A3 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR.  * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG.",
         "license": "proprietary"
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         "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43A3)(https://doi.org/10.5067/MODIS/MCD43A3.061) and BRDF (MCD43A4)  (https://doi.org/10.5067/MODIS/MCD43A4.061) products.    The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the quality layers for each of the 10 bands.     Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
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         "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.    The MCD43A4 provides NBAR and quality layers for MODIS bands 1 through 7.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
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         "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C3 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Albedo dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models.     MCD43C3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) at local solar noon. Black-sky albedo and white-sky albedo values are available as a separate layer for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the 20 albedo layers are ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
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         "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models.     MCD43C4 removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. These NBAR values are calculated from MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061). The product includes separate NBAR layers for MODIS spectral bands 1 through 7 as well as ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).      ",
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         "description": "The MCD43D03 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D03 is the Bidirectional Reflectance Distribution Function geometric parameter for MODIS band 1. This geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the Albedo and BRDF value for MODIS band 1. ",
         "license": "proprietary"
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         "description": "The MCD43D04 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter data set is a daily 16-day product. This product incorporates the Climate Modeling Grid (CMG) structure in which each file geographically covers the entire earth rather than the 10 degree x 10 degree latitude and longitude tiling system utilized by the standard MODIS land products. Unlike the standard CMG pixel resolution of 5600 meters the MCD43D products are 1000 meters, consequently, because of the large file size each product contains just one layer. The Julian date in the granule ID of each specific file represents the 9th day of the 16 day composite period, and consequently the observations are weighted to estimate the BRDF/Albedo for that day. The layer in the MCD43D04 is the Bidirectional Reflectance Distribution Function isotropic parameter for MODIS band 2. This isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the Albedo and BRDF value for MODIS band 2. ",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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@@ -125315,7 +124977,7 @@
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         "description": "The MCD43D37 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) QA ValidObs Band 5 dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D37 provides MODIS band 5 valid observation quality information for the MCD43D products.     MCD43D37 contains the valid observation quality layer representing each of the 16 days of the retrieval period for MODIS band 5.      Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
         "license": "proprietary"
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@@ -125328,7 +124990,7 @@
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The MCD43D44 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Black-Sky Albedo dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer.     MCD43D42 through MCD43D61 are the albedo products of the MCD43D BRDF/Albedo product suite. There are 10 black-sky albedo and 10 white-sky albedo layers representing MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic.     MCD43D44 is the black-sky albedo for MODIS band 3.     Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The MCD43D66 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer.     MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.    MCD43D66 is the NBAR for MODIS band 5.     Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The MCD43D67 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer.     MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.    MCD43D67 is the NBAR for MODIS band 6.     Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
         "license": "proprietary"
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         "description": "The MCD43D68 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer.     MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.    MCD43D68 is the NBAR for MODIS band 7.     Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6  is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands.     The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data.    Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006).    The MCD43 product is not recommended for solar zenith angles beyond 70 degrees.    The MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.    Improvements/Changes from Previous Versions    Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period.  *\tMCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period.  *\tBetter quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day.  *\tThe MCD43 products use L2G-lite surface reflectance as input.  *\tIn cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel.  *\tCMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo.    Important Quality Information  The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.        ",
         "license": "proprietary"
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         "description": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells.     The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.     Validation at stage 3 ( https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Burned Area product. Further details regarding MODIS land product validation for the MCD64A1 data product is available from the MODIS Land Team Validation site ( https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD64).          Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
         "license": "proprietary"
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         "description": "The Integrated Multi-Mission Ocean Altimeter Sea Surface Height (SSH) Version 5.2 dataset provides level 2 along track sea surface height anomalies (SSHA) for 10-day cycles from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A missions geo-referenced to a mean reference orbit. It is produced by NASA Sea Surface Height (NASA-SSH) project investigators at Goddard Space Flight Center and Jet Propulsion Laboratory with support from NASA\u2019s Physical Oceanography program, and was developed originally as an Earth System Data Record (ESDR) under the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, which supported forward processing and incremental refinements through version 5.1 (released in April 2022).<br>  Geophysical Data Records (GDRs) from each altimetry mission were interpolated to a common reference orbit with biases and cross-calibrations applied so that the derived SSHA are consistent between satellites to form a single homogeneous climate data record. The entire multi-mission data record covers the period from September 1992 to present; it is extended to include new observations approximately once each quarter. The previous release (version 5.1) integrated Jason-3 data and applied revised internal tides and pole tide across missions (GDR_F standard). The current release (version 5.2) includes the following revisions: a) GSFC std2006_cs21 orbit for all missions, b) GOT5.1 ocean tide model, c) TOPEX/Poseidon GDR_F data, d) Sentinel-6 LR version F08 data, e) Jason-3 re-calibrated radiometer wet troposphere correction. More information about the data content and derivation can be found in the v5.2 User\u2019s Handbook (https://doi.org/10.5067/ALTUG-TJ152).<br>  Please note that this collection contains the same data as https://doi.org/10.5067/ALTTS-TJA52, re-organized into one netCDF file per cycle for convenience.",
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The MOD11B1 Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
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         "description": "The MOD11B2 Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MOD11B2 is a simple average of all the corresponding values from the LST&E values from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) product collected during that 8-day period. Each MOD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788885-LPCLOUD.html",
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         "description": "The MOD11B3 Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MOD11B3 is a simple average of all the corresponding values from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) collected during the month period. Each MOD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MOD11_L2 (https://doi.org/10.5067/MODIS/MOD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).   Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
@@ -131269,7 +130931,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788888-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788888-LPCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvYXF1YSB3YXRlciByZXNlcnZvaXIgOC1kYXkgbDMgZ2xvYmFsIHYwNjFcIixcIkxQQ0xPVURcIixcIk1ZRDI4QzJcIixcIjYxXCIsMjU2NTgwNTgxOCwxNl0iLCJ1bW0iOiJbXCJtb2Rpcy9hcXVhIHdhdGVyIHJlc2Vydm9pciA4LWRheSBsMyBnbG9iYWwgdjA2MVwiLFwiTFBDTE9VRFwiLFwiTVlEMjhDMlwiLFwiNjFcIiwyNTY1ODA1ODE4LDE2XSJ9/MOD11C1_061",
         "description": "The MOD11C1 Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MOD11C1 product is directly derived from the MOD11B1 (https://doi.org/10.5067/MODIS/MOD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MOD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
@@ -131282,7 +130944,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788893-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788893-LPCLOUD.html",
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         "description": "The MOD11C2 Version 6.1 product provides  Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MOD11C2 product are derived by compositing and averaging the values from the corresponding eight MOD11C1 (https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. The MOD11C2 granule consists of 17 layers. Each MOD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
@@ -131295,7 +130957,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565788897-LPCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvYXF1YSB3YXRlciByZXNlcnZvaXIgOC1kYXkgbDMgZ2xvYmFsIHYwNjFcIixcIkxQQ0xPVURcIixcIk1ZRDI4QzJcIixcIjYxXCIsMjU2NTgwNTgxOCwxNl0iLCJ1bW0iOiJbXCJtb2Rpcy9hcXVhIHdhdGVyIHJlc2Vydm9pciA4LWRheSBsMyBnbG9iYWwgdjA2MVwiLFwiTFBDTE9VRFwiLFwiTVlEMjhDMlwiLFwiNjFcIiwyNTY1ODA1ODE4LDE2XSJ9/MOD11C3_061",
         "description": "The MOD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05  degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MOD11C3 product are derived by compositing and averaging the values from the corresponding month of MOD11C1 (https://doi.org/10.5067/MODIS/MOD11C1.061) daily files. Each MOD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  ",
         "license": "proprietary"
     },
@@ -131334,7 +130996,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2343115255-LPCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvYXF1YSB3YXRlciByZXNlcnZvaXIgOC1kYXkgbDMgZ2xvYmFsIHYwNjFcIixcIkxQQ0xPVURcIixcIk1ZRDI4QzJcIixcIjYxXCIsMjU2NTgwNTgxOCwxNl0iLCJ1bW0iOiJbXCJtb2Rpcy9hcXVhIHdhdGVyIHJlc2Vydm9pciA4LWRheSBsMyBnbG9iYWwgdjA2MVwiLFwiTFBDTE9VRFwiLFwiTVlEMjhDMlwiLFwiNjFcIiwyNTY1ODA1ODE4LDE2XSJ9/MOD11_L2_061",
         "description": "The MOD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MOD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MOD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples.  Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MOD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
@@ -131607,7 +131269,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791029-LPCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvYXF1YSB3YXRlciByZXNlcnZvaXIgOC1kYXkgbDMgZ2xvYmFsIHYwNjFcIixcIkxQQ0xPVURcIixcIk1ZRDI4QzJcIixcIjYxXCIsMjU2NTgwNTgxOCwxNl0iLCJ1bW0iOiJbXCJtb2Rpcy9hcXVhIHdhdGVyIHJlc2Vydm9pciA4LWRheSBsMyBnbG9iYWwgdjA2MVwiLFwiTFBDTE9VRFwiLFwiTVlEMjhDMlwiLFwiNjFcIiwyNTY1ODA1ODE4LDE2XSJ9/MOD17A2HGF_061",
         "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.  The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H (https://doi.org/10.5067/modis/mod15a2h.061) is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation  (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.  Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.    ",
         "license": "proprietary"
     },
@@ -131620,7 +131282,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791027-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565791027-LPCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvYXF1YSB3YXRlciByZXNlcnZvaXIgOC1kYXkgbDMgZ2xvYmFsIHYwNjFcIixcIkxQQ0xPVURcIixcIk1ZRDI4QzJcIixcIjYxXCIsMjU2NTgwNTgxOCwxNl0iLCJ1bW0iOiJbXCJtb2Rpcy9hcXVhIHdhdGVyIHJlc2Vydm9pciA4LWRheSBsMyBnbG9iYWwgdjA2MVwiLFwiTFBDTE9VRFwiLFwiTVlEMjhDMlwiLFwiNjFcIiwyNTY1ODA1ODE4LDE2XSJ9/MOD17A2H_061",
         "description": "The MOD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.     Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MOD17 products.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.   ",
         "license": "proprietary"
     },
@@ -131646,7 +131308,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2545303088-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2545303088-LPCLOUD.html",
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
         "license": "proprietary"
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at  stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).  ",
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21C1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MOD21 (http://doi.org/10.5067/MODIS/MOD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).   ",
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MOD21C3 dataset is a monthly composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MOD21A1D (http://doi.org/10.5067/MODIS/MOD21A1D.061) and MOD21A1N (http://doi.org/10.5067/MODIS/MOD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MOD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
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         "description": "The MOD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MOD21 Land Surface Temperature (LST) algorithm differs from the MOD11 (https://doi.org/10.5067/modis/mod11_l2.061) algorithm in that the MOD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).      Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MOD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The product utilizes GEOS data replacing MERRA2.   * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).           ",
         "license": "proprietary"
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         "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MOD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.  The MOD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves (https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Terra satellite (MOD09Q1).   The MOD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity.  ",
         "license": "proprietary"
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         "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MOD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MOD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MOD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) (https://www.sciencedirect.com/science/article/pii/S0034425720304776?via%3Dihub) via MODIS Land Surface Temperature (LST) (MOD21) and meteorological data from Global Land Data Assimilation System (GLDAS) (https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MOD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume.",
         "license": "proprietary"
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         "description": "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask (MOD44W) Version 6.1 data product provides a global map of surface water at 250 meter (m) spatial resolution. The data are available annually from 2000 to present. MOD44W Version 6.1 is derived using a decision tree classifier trained with MODIS data and validated with the Version 5 MOD44W data product. A series of masks are applied to address known issues caused by terrain shadow, burn scars, cloudiness, or ice cover in oceans. Version 6.1 is the generation of a time series rather than a simple static representation of water, given that water bodies fluctuate in size and location over time due to both natural and anthropogenic causes. Provided in each MOD44W Version 6.1 Hierarchical Data Format 4 (HDF4) file are layers for land water mask and water body classification. A quality assurance (QA) layer provides users with information on the determination of water. The new seven class water classification layer provides values for shallow ocean, land, shoreline, inland water, ephemeral water, deep inland water, moderate ocean, deep ocean, and a classification deemed to fall outside of the projection.    Improvements/Changes from Previous Versions    * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * Additional data files will be produced annually and are scheduled for distribution in the first quarter of the calendar year.   * Seven-class water body classification layer produced at 250 meter resolution is provided in tiled format.  * For Collection 6.1 and beyond, Antarctica data are now being produced.   ",
         "license": "proprietary"
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         "description": "NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 1:30 pm, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions.  Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron  wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day.  The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST.  The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDA-2PJ02",
         "license": "proprietary"
     },
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         "description": "Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite.  Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions.  Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron  wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron  wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection.  Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS)  are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST.  The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODAM-8D4N4",
         "license": "proprietary"
     },
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         "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite.  Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron  channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be at https://doi.org/10.5067/MODST-MO9D4",
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         "description": "Day and night spatially gridded global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite.  Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron  channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be at https://doi.org/10.5067/MODST-MO9N4",
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         "description": "The GHRSST L2P MSG01 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-8 (MSG1) satellite. It provides the full disk SEVIRI imagery covering the Indian Ocean region from its position at 45.5\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. The full data records stretch from Sept. 18, 2018 to June 1, 2022. After June 1, 2022, the Meteosat-9 (MSG2) took over as the prime geostationary satellite for the Indian Ocean region (MSG02-OSPO-L2P-v1.0). Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br>    The SST measurements from SEVIRI are key parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.  <br><br>    This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Meteosat Second Generation (MSG-3) satellites  are spin stabilized geostationary satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) to provide accurate weather monitoring data through its primary instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in 12 spectral channels. Eight of these channels are in the thermal infrared, providing among other information, observations of the temperatures of clouds, land and sea surfaces at approximately 5 km resolution with a 15 minute duty cycle. This Group for High Resolution Sea Surface Temperature (GHRSST) dataset produced by the US National Oceanic and Atmospheric Administration (NOAA)  National Environmental Satellite, Data, and Information Service (NESDIS) is derived from the SEVIRI instrument on the second MSG satellite (also known as  Meteosat-9) that was launched on 22 December 2005. Skin sea surface temperature (SST) data are calculated from the infrared channels of SEVIRI at full resolution every 15 minutes. L2P data products with Single Sensor Error Statistics (SSES) are then derived following the GHRSST-PP Data Processing Specification (GDS) version 2.0.",
         "license": "proprietary"
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         "description": "The GHRSST L2P MSG04 SST v1.0 dataset is produced by the US National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat-11 (MSG4) satellite. It provides the full disk SEVIRI imagery covering the Atlantic Ocean region from its position at 0.0\u00b0E longitude. The L2P SST is produced at approximately 3 km resolution with a 15 minute duty cycle. On Feb. 2, 2018 the Meteosat-11 (MSG4) took over the Meteosat-10 (MSG3) (MSG03-OSPO-L2P-v1.0) and produced the L2P SST data from Sept 10. 2018 to March 24, 2023. In March 2023, Meteosat-10 and Meteosat-11 were swapped roles and orbital positions. The MSG03 has started to produce the L2P SST data again over the Atlantic Ocean region. Be aware that the granules before Dec. 1, 2022 contain some uncorrected metadata errors. <br><br>    The SST measurements from SEVIRI are parameters in study of the weather, atmosphere, climate and ocean environments. Meteosat satellites have been providing crucial data for weather forecasting since 1977.  <br><br>    This L2P SST product which includes Single Sensor Error Statistics (i.e., uncertainty statistics) follows the GHRSST Data Processing Specification (GDS) version 2.0 format guidelines. Please refer to the user guide for more information.",
         "license": "proprietary"
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         "description": "The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping.     Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.  ",
         "license": "proprietary"
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         "description": "Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes.  The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata \"history:\" attribute to determine if a granule is near-realtime or retrospective.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset  at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) global Level 4 sea surface temperature analysis produced daily on a 0.09-degree grid at Remote Sensing Systems. This product uses optimal interpolation (OI) from both microwave (MW) sensors including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, and WindSat operates on the Coriolis satellite, and infrared (IR) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platform and the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite.  The through-cloud capabilities of microwave radiometers provide a valuable picture of global sea surface temperature (SST) while infrared radiometers (i.e., MODIS) have a higher spatial resolution. This analysis does not use any in situ SST data such as drifting buoy SST. Comparing with previous version 4.0 dataset, the version 5.0 has made the updates in several areas, including the diurnal warming model, the sensor-specific error statistics (SSES) for each microwave sensor, the sensor correlation model, and the quality mask.",
         "license": "proprietary"
     },
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The MYD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1748046084-LPCLOUD.html",
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         "description": "The MYD11A1 Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2269057787-LPCLOUD.html",
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         "description": "The MYD11A2 Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 (https://doi.org/10.5067/MODIS/MYD11A1.061) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.  Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include:  changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794007-LPCLOUD.html",
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         "description": "The MYD11B1 Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
@@ -135832,7 +135494,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794018-LPCLOUD.html",
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         "description": "The MYD11B2 Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 (https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
@@ -135845,7 +135507,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794030-LPCLOUD.html",
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         "description": "The MYD11B3 Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km [MYD11_L2](https://doi.org/10.5067/MODIS/MYD11_L2.061) swath product aggregated to the 6 km grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
@@ -135858,7 +135520,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794038-LPCLOUD.html",
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         "description": "The MYD11C1 Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the MYD11B1 (https://doi.org/10.5067/MODIS/MYD11B1.061) product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794042-LPCLOUD.html",
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         "description": "The MYD11C2 Version 6.1 product provides  Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794044-LPCLOUD.html",
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         "description": "The MYD11C3 Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05  degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of MYD11C1 (https://doi.org/10.5067/MODIS/MYD11C1.061) daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32.   Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2343114808-LPCLOUD.html",
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         "description": "The MYD11_L2 Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples.  Validation at stage 2 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Land Surface Temperature and Emissivity products. Further details regarding MODIS land product validation for the MYD11 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD11)  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
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         "description": "The MYD13A1 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.   Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
@@ -135962,7 +135624,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794049-LPCLOUD.html",
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         "description": "The MYD13A2 Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.   Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
     },
@@ -135975,7 +135637,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2327957988-LPCLOUD.html",
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average.   The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.  Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion.  Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers.  Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).   Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
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         "description": "The MYD13C1 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.  The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution.   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
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         "description": "The MYD13C2 Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions.  The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 (https://doi.org/10.5067/MODIS/MYD13A2.061) products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution.    Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.  Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Vegetation Index product suite. Further details regarding MODIS land product validation for the MOD13 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). ",
         "license": "proprietary"
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.  The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.    Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition.  The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.  Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
     },
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MYD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at a 1 kilometer (km) spatial resolution. The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide).   Validation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for all MODIS Thermal Anomalies and Fire products. Further details regarding MODIS land product validation for the MOD14 data product is available from the MODIS land team validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD14).  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the \u201cbest\u201d pixel available from all the acquisitions of the Aqua sensor from within the 8-day period.  LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy.  Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule.  The LAI product has attained stage 2 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation and the FPAR product has attained stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
         "license": "proprietary"
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         "description": "The MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day  composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.  The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year.  Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule.  The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year.  Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
         "license": "proprietary"
     },
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         "description": "The MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.  Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule.  The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year.  Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.",
         "license": "proprietary"
     },
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         "description": "The MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover.  The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/MODIS/MYD15A2H.061) is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year.  Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule.  The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.  Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for MODIS Evapotranspiration products.  Improvements/Changes from Previous Changes * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
         "license": "proprietary"
     },
@@ -136144,7 +135806,7 @@
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         "description": "The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.  The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year.  Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
         "license": "proprietary"
     },
@@ -136157,7 +135819,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565794796-LPCLOUD.html",
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         "description": "The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.   Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.  Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. ",
         "license": "proprietary"
     },
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         "description": "The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H)(https://doi.org/10.5067/MODIS/MYD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).    The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the  Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year.    Stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.    Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.        ",
         "license": "proprietary"
     },
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         "description": "A suite of MODIS Land Surface Temperature and Emissivity  (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.     The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).    Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).    Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).  ",
         "license": "proprietary"
     },
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MYD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
         "license": "proprietary"
     },
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         "description": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.     The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (httpd://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).    Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).    Improvements/Changes from Previous Versions  * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.  * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).  * The product utilizes GEOS data replacing MERRA2.  * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).  ",
         "license": "proprietary"
     },
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         "description": "A new suite of MODIS Land Surface Temperature and Emissivity  (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 (https://doi.org/10.5067/MODIS/MYD21.061) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
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         "description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (https://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
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         "description": "A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions.   The MYD21C3 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D (https://doi.org/10.5067/MODIS/MYD21A1D.061) and MYD21A1N (http://doi.org/10.5067/MODIS/MYD21A1N.061) daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).  Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3).",
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         "description": "The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 (https://doi.org/10.5067/modis/myd11_l2.061) algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD (https://lpdaac.usgs.gov/documents/1399/MOD21_ATBD.pdf)).    Validation at stage 1 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Land Surface Temperature and Emissivity data products. Further details regarding MODIS land product validation for the MYD21 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD21).  Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The product utilizes GEOS data replacing MERRA2. * Three new CMG products are available in the MxD21 suite (MxD21C1/C2/C3). ",
         "license": "proprietary"
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs.    The MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves (https://doi.org/10.1016/j.rse.2020.111831) and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Aqua satellite (MYD09Q1).     The MYD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity.   ",
         "license": "proprietary"
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         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MYD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) (https://www.sciencedirect.com/science/article/pii/S0034425720304776?via%3Dihub) via MODIS Land Surface Temperature (LST) (MYD21) and meteorological data from Global Land Data Assimilation System (GLDAS) (https://earth.gsfc.nasa.gov/hydro/data/gldas-global-land-data-assimilation-system-data) are used to produce monthly evaporation rates and volume losses. The MYD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume.",
         "license": "proprietary"
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         "description": " Marine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features (clear & turbid water, waves, etc.) and floating materials (Sargassum macroalgae, ships, natural organic material, etc) that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task. ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412735-MLHUB.html",
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         "description": " Floating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris on satellite imagery and pave the way to a global monitoring system for controlling and preventing the accumulation of marine debris in oceans. This dataset consists of images of marine debris which are 256 by 256 pixels in size and labels which are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery which has a spatial resolution of approximately 3 meters. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items. Several studies were used for data collection and validation. While a small percentage of the dataset represents the coastlines of Ghana and Greece, most of the observations surround the Bay Islands in Honduras. The marine debris detection models created and the relevant code for using this dataset can be found [here](https://github.com/NASA-IMPACT/marine_debris_ML). ",
         "license": "proprietary"
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         "description": "The N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.  <br><br>    VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system (Jonasson et al. 2022). Data are reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. In ACSPO products, SSTs are derived using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels with quality level QL=5 are recommended. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. <br><br>    The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO N21 VIIRS L3U product is also available  (10.5067/GHV21-3U280) (Ignatov et al., 2017).",
         "license": "proprietary"
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         "description": "The N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.  <br><br>    The ACSPO N21 VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N21 VIIRS L2P product, also available at PO.DAAC (10.5067/GHV21-2P280). The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The  ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. There are 144 granules per 24 hour interval, with a total data volume of 0.6GB/day. Fill values are reported at all invalid pixels, including pixels >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SST, a subset of variable l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and the SST minus reference CMC SST  (Canadian Met Centre 0.1deg L4 SST, 10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. <br><br>    The ACSPO VIIRS L3U product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM).",
         "license": "proprietary"
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         "catalog": "LARC_ASDC STAC Catalog",
         "state_date": "2015-11-04",
         "end_date": "2018-03-25",
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1688110309-LARC_ASDC.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688110309-LARC_ASDC.html",
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688110103-LARC_ASDC.html",
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         "license": "proprietary"
     },
@@ -137704,7 +137366,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688110467-LARC_ASDC.html",
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         "description": "NAAMES_Aerosol_ShipInSitu_Data are in situ aerosol measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
     },
@@ -137717,7 +137379,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688111638-LARC_ASDC.html",
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         "description": "NAAMES_Cloud_AircraftInSitu_Data are in situ cloud measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.     The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2303156583-LARC_ASDC.html",
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         "description": "NAAMES_Merge_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) pre-generated aircraft merge data files created using data collected during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
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         "description": "NAAMES_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "description": "NAAMES_MetNav_ShipInSitu_Data are in situ meteorological and navigational measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688124829-LARC_ASDC.html",
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         "description": "NAAMES_Met_SondeInSitu_Data are meteorological radiosonde measurements collected via radiosonde launches during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015 and May 11, 2016 \u2013 June 5 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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@@ -137782,7 +137444,7 @@
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         "description": "NAAMES_Misc_Ship_Data are miscellaneous ship measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
     },
@@ -137795,7 +137457,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1688126075-LARC_ASDC.html",
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         "description": "NAAMES_Ocean_AircraftRemoteSensing_Data are remotely sensed ocean measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Related ocean property measurements are available in the NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "title": "NAAMES C-130 Aircraft In-Situ Radiation Data",
         "catalog": "LARC_ASDC STAC Catalog",
         "state_date": "2015-11-04",
-        "end_date": "2017-09-20",
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         "description": "NAAMES_Radiation_AircraftInSitu_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in-situ radiation data collected onboard the C-130 aircraft during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "description": "NAAMES_TraceGas_AircraftInSitu_Data are in situ trace gas measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.     The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "description": "NAAMES_TraceGas_ShipInSitu_Data are in situ trace gas measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 \u2013 November 29, 2015, May 11, 2016 \u2013 June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 \u2013 April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture \u2013 Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 \u2013 December 2, 2015), the Bloom Climax (May 11 \u2013 June 5, 2016), the Deceleration Phase (August 30 \u2013 September 24, 2017), and the Acceleration Phase (March 20 \u2013 April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.",
         "license": "proprietary"
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         "description": "The NARSTO_EPA_SS_ATLANTA_1999_CHEM_PM_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 Air Chemistry, Particulate Matter (PM), and Meteorological Data product. This data product was obtained from July to September 1999 during the Atlanta Experiment of the U.S. EPA Particulate Matter Supersites Program.     The EPS selected Atlanta as one of the first Supersites Programs dedicated to the study of fine particles (or Particulate Matter (PM) 2.5). The Southern Oxidants Study (SOS) in conjunction with the Georgia Institute of Technology, Earth and Atmospheric Sciences Department developed and implemented the scientific research plan for this initial Supersites Program effort. The Atlanta field experiment was a 4-week long campaign aimed at comprehensively addressing issues related to the measurement and characterization of fine particles in the polluted or urban atmosphere. The experiment took place during the August 1999 and deployed a wide array of instrumentation at a measurement site located on Jefferson Street in Midtown Atlanta.    Goals of the Atlanta Supersite Program were twofold: first, to provide a platform for testing and contrasting some of the newer particle measurement techniques; and second, to provide data to advance our scientific understanding of atmospheric processes regarding atmospheric particles. Specific objectives were: (1) to characterize the performance of emerging and/or state-of-the-science PM Measurements; (2) to compare and contrast similar and dissimilar PM Measurements; (3) to evaluate the precision, accuracy, and completeness of information that can be gained from the planned EPA PM mass and chemical composition networks; (4) to evaluate the scientific information gained by combining various independent and complementary PM Measurements; and (5) to address various scientific issues and their ozone- and PM-related policy implications with this data base.    The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
         "license": "proprietary"
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         "description": "NARSTO_EPA_SS_ATLANTA_1999_UAH_MIPS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 University of Alabama-Huntsville (UAH) Mobile Integrated Profiling System (MIPS) Wind Data product. Files for this data product were obtained from July to September 1999 during the Atlanta Experiment of the EPA Particulate Matter Supersites Program. The UAH MIPS Doppler profiler (915 MHz radar) was used to estimate the vertical distribution of horizontal wind speed and wind direction. Radial velocity along six beams was used to obtain the horizontal wind speed and wind direction. The consensus averaging time was 55 minutes, the number of beams is 6, and the number of range gates was 41. For beam 1, the number of records required to make consensus was 16, the total number of records was 26, and the consensus window size was 4 m/s. For beams 2 and 3, the number of records required to make consensus was 13, the total number of records was 26, and the consensus window size was 3 m/s. There was no data for beams 4 and 5. For beam 6, the number of records required to make consensus was 16, the total number of records was 26 and the consensus window size was 3 m/s. The azimuth and elevation for beams 1 to 6 were: 358 and 90; 88 and 66.4; 178 and 66.4; none; none; 88 and 90.    The EPA selected Atlanta as one of the first Supersites Programs dedicated to the study of fine particles (or Particulate Matter (PM) 2.5). The Southern Oxidants Study (SOS) in conjunction with the Georgia Institute of Technology, Earth and Atmospheric Sciences Department developed and implemented the scientific research plan for this initial Supersites Program effort. The Atlanta field experiment was a 4-week long campaign aimed at comprehensively addressing issues related to the measurement and characterization of fine particles in the polluted or urban atmosphere. The experiment took place during the August 1999 and deployed a wide array of instrumentation at a measurement site located on Jefferson Street in Midtown Atlanta.     Goals of the Atlanta Supersite Program were twofold: first, to provide a platform for testing and contrasting some of the newer particle measurement techniques; and second, to provide data to advance our scientific understanding of atmospheric processes regarding atmospheric particles. Specific objectives were: (1) to characterize the performance of emerging and/or state-of-the-science PM Measurements; (2) to compare and contrast similar and dissimilar PM Measurements; (3) to evaluate the precision, accuracy, and completeness of information that can be gained from the planned EPA PM mass and chemical composition networks; (4) to evaluate the scientific information gained by combining various independent and complementary PM Measurements; and (5) to address various scientific issues and their ozone- and PM-related policy implications with this data base.    The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
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         "description": "NARSTO_EPA_SS_ATLANTA_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Atlanta 1999 Rapid Single-Particle Mass Spectrometer (SPMS) Data product. Data for this product was obtained in August 1999 during the Atlanta Experiment of the EPA Particulate Matter (PM) Supersites Program.     During a month in the summer of 1999, individual aerosol particles were sized and analyzed using a Rapid Single-particle Mass Spectrometer (RSMS) in Atlanta. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a single time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were initiated periodically, and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range.     Single particle data are valuable for the following reasons:  -\tthey are collected and analyzed real time so have excellent temporal resolution,   -\tthe particle-to-particle composition variations (external mixing properties) can be assessed, and   -\tkey particle sources are easily identified since the particles retain source characteristics.     The data resulting from these measurements consist of an aerodynamic particle size and a positive mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy. Support for RSMS measurements was provided by the EPA Supersite program and additional funding from the EPA and National Science Foundation (NSF).    The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
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         "description": "The NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Johns Hopkins University (JHU) LIDAR Backscatter and Mixing Height Data product. This product contains measurements that were taken from May 2001 to September 2002 during the Baltimore Experiment of the EPA Particulate Matter (PM) Supersites Program by the JHU, Department of Geography and Environmental Engineering. A miniature elastic backscatter LIDAR was operated at several Baltimore locations in the vertical mode with typical resolution range of 3 m, typical time steps of 5 seconds, and ranges of 4.5-8 km. All vertical profile measurements of aerosol backscatter were taken during daytime with continuous sampling and can be used to describe composition, dynamics, and extent of the mixing layer and the air aloft. Mixing heights were determined from profile data under cloud-free conditions. Included in this data set are the large ASCII files of the aerosol backscatter data, the calculated mixing height data, and a companion HTML application with color images of the LIDAR profiles of the backscatter signals from aerosols. The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities.     The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
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         "description": "The NARSTO_EPA_SS_BALTIMORE_JHU_MET_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Johns Hopkins University Meteorolgical Data product. This product contains  meteorological and turbulence measurements that were recorded using a diverse array of instruments by the Parlange Environmental Fluid Mechanics Group, Department of Geography and Environmental Engineering, JHU at the EPA Baltimore Supersite. Measurements were made at three Baltimore locations over the indicated time intervals: FMC Corporation (May 26 - June 15, 2001), Clifton Park (July 1 - September 14, 2001), and Ponca Street (February 13, 2002 - March 15, 2003).    The instruments were mounted on an 11m tall meteorological tower on the site. The instrumentation consisted of a 3d sonic anemometer-thermometer, pyranometer, wind vane, tipping bucket rain collector, 2 cup anemometers, temperature and relative humidity probe and pressure sensor. The data were collected on a continuous basis and were subsequently subjected to multiple cycles of data validation to ensure correctness and accuracy. The validated data was then averaged over a 5 minute interval to create the final data set. The data set is organized to provide a unique data file for any given day within the operating time duration. Each file contains the variables temperature, relative humidity, mean horizontal wind speed (at 10.39m), horizontal resultant vector mean wind speed, mean horizontal wind speed (at 5.87m), mean horizontal wind angle, std deviation of the wind angle, precipitation, friction velocity, Obukhov length, sensible vertical heat flux, solar radiation, atmospheric pressure, virtual potential temperature, specific humidity and wind angle from sonic anemometer. In addition to usual meteorological variables, this data set also provides information on turbulent mixing (parameterized by the friction velocity) and atmospheric stability (parameterized by the Obukhov length).     The Baltimore Supersite collected high-quality ambient air quality measurements with unprecedented temporal resolution at an industrially influenced urban site and two intensive measurement campaigns. A data set of project results was constructed to take advantage of advanced multivariate statistical techniques. Data were collected on the sources and nature of organic aerosol for the region, and large quantities of urban particulate matter (PM) were collected for retrospective chemical, physical, and biological analyses and for toxicological testing. These data provided important information on the potential health effects of particles to support exposure and epidemiological studies for enhanced evaluation of health outcome, pollutant, and source relationships.     The EPA PM Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
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         "description": "NARSTO_EPA_SS_BALTIMORE_RAPID_SPMS_DATA is the North American Research Strategy for Tropospheric Ozone (NARSTO) Environmental Protection Agency (EPA) Supersite (SS) Baltimore, Rapid Single-Particle Mass Spectrometer (RSMS) Data product. This data product was  obtained in 2002 at the Baltimore Supersite. For 7 months, starting in May 2002, individual aerosol particles were sized and analyzed using a RSMS in Baltimore. RSMS aerodynamically focuses one particle size at a time to the source region of a mass spectrometer and employs a 193 nm excimer laser to desorb and ionize the particle components. The ions are analyzed in a dual time-of-flight mass spectrometer and the spectrum is digitally recorded. Spectra are only saved if the ion peak in the spectrum is above a threshold level. Background spectra were determined and flagged. Particle size scans were periodically initiated and each size was sampled until 30 particle hits were obtained, unless the sampling time became excessive. Aerodynamic particle sizes ranged from about 40 to 1300 nm and were partitioned into nine discrete size classes logarithmically spaced, roughly, over the range.     Single particle data are valuable because for the following reasons:   -\tthey are collected and analyzed real time so have excellent temporal resolution,   -\tthe particle-to-particle composition variations (external mixing properties) can be assessed, and   -\tkey particle sources are easily identified since the particles retain source characteristics.    The data resulting from these measurements consist of an aerodynamic particle size and a positive and negative mass spectrum of the components for each particle, along with the date and time of measurement and other incidental measurement parameters such as the laser pulse energy. Support for RSMS measurements were provided by the EPA Supersites program and additional funding from the EPA.    The EPA PM Supersites Program was an ambient air monitoring research program from 1999-2004 designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address the following EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods.    NARSTO, which has since disbanded, was a public/private partnership, whose membership spanned across government, utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission was to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are still available.",
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         "description": "This dataset contains synthetic aperture radar (SAR) raster imagery for various flood events acquired from the European Space Agencys Sentinel-1A and Sentinel-1B missions, providing C-Band dual-polarized imagery that spans geographical areas of interest in the United States and Bangladesh. The main emphasis was on the labeling of open water areas where specular reflection of the radar signal off of the relatively still, flat open water surface results in reduced backscatter, low amplitude, and an overall darkened appearance within the image. The labels for the water surface reflectance are also provided in GeoTiff rasterized file format in scenes aligned with the SAR source raster imagery.",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_HGT) dataset, which provides global elevation data at 1 arc second spacing.    NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days.   In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy.  Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.    NASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass.       NASADEM_HGT data product layers include DEM, number of scenes (NUM), and an updated SRTM water body dataset (water mask). The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data. A low-resolution browse image showing elevation is also available for each NASADEM_HGT granule.  ",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_NC) dataset, which provides global elevation data at 1 arc second spacing.    NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days.   In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy.  Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.    NASADEM are distributed in 1\u00b0 by 1\u00b0 tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass.       NASADEM_NC data product layer includes DEM. The NUM layer indicates the number of scenes that were processed for each pixel and the source of the data. The source of each elevation pixel in the corresponding NASADEM_NUMNC product.  ",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_NC) dataset, which provides global elevation data at 1 arc second spacing.    NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days.   In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy.  Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.    NASADEM are distributed in 1\u00b0 by 1\u00b0 tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass.       NASADEM_NC data product layer includes DEM. The NASADEM_NCNUM layer indicates the number of scenes that were processed for each pixel and the source of the data.    The global 1 arc second NASADEM product is also available in NetCDF4 format as the NASADEM_NC dataset with the source of each elevation pixel in the corresponding NASADEM_NUMNC product.  ",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SIM) dataset, which provides global Shuttle Radar Topography Mission (SRTM) image mosaic data at 1 arc second spacing.    NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM\u2019s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and \ufb02ew for 11 days.   In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy.  Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling.    NASADEM are distributed in 1 degree latitude by 1 degree longitude tiles and consist of all land between 60\u00b0 N and 56\u00b0 S latitude. This accounts for about 80% of Earth\u2019s total landmass.       NASADEM_SIM data product layers include radar combined images and a NUM file associated with combined images. A low-resolution browse image showing the SRTM image mosaic elevation is also available for each NASADEM_SIM granule.  ",
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         "license": "proprietary"
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         "description": "This NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 4.45 km pixel resolution stored in SIR files; polar regions are gridded using a polar-stereographic technique. A non-enhanced version is provided at 22.25 km pixel resolution in a format known as GRD files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, polarization, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In additional to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. For more information, please visti: http://www.scp.byu.edu/docs/NSCAT_user_notes.html",
         "license": "proprietary"
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         "description": "The NASA Scatterometer (NSCAT) Level 1.7 ocean sigma-0 referenced to 50 km wind vector cells (WVC) contains daily backscatter (sigma-0) data from ascending and descending passes. Rain flagging information is not included. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; re-processing had only minor impacts on the Level 1.7 data.",
         "license": "proprietary"
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         "description": "The NASA Scatterometer (NSCAT) Level 2 ocean wind vectors in 50 km wind vector cell (WVC) swaths contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea ice. Winds are calculated using the NSCAT-2 model function. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.",
         "license": "proprietary"
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         "description": "This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203.",
         "license": "proprietary"
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         "description": "The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function.",
         "license": "proprietary"
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         "description": "The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537004985-POCLOUD.html",
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         "description": "This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.1Mv03 dataset, which can be found at https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table.",
         "license": "proprietary"
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         "description": "This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015  present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625deg. x 0.0625deg. horizontal resolution over the Mediterranean Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.",
         "license": "proprietary"
     },
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         "description": "CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01deg. horizontal resolution over the Mediterranean    Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Mediterranean sea.",
         "license": "proprietary"
     },
@@ -150292,19 +149954,6 @@
         "description": "This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.",
         "license": "proprietary"
     },
-    {
-        "id": "OMCLDRR_003",
-        "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC",
-        "catalog": "GES_DISC STAC Catalog",
-        "state_date": "2004-10-01",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.html",
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-        "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering).  This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.  The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.",
-        "license": "proprietary"
-    },
     {
         "id": "OMCLDRR_003",
         "title": "OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT",
@@ -150318,6 +149967,19 @@
         "description": "The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/",
         "license": "proprietary"
     },
+    {
+        "id": "OMCLDRR_003",
+        "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC",
+        "catalog": "GES_DISC STAC Catalog",
+        "state_date": "2004-10-01",
+        "end_date": "",
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+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json",
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+        "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering).  This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.  The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.",
+        "license": "proprietary"
+    },
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         "id": "OMCLDRR_004",
         "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset contains Level 2 in situ temperature profile measurements from the Airborne eXpendable BathyThermograph (AXBT) probes. It provides science quality temperature measurements as a function of depth in the water column. The AXBTs were jettisoned from a plane to collect temperature readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXBTs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature as it falls.  An equation is used to determine the depth of the measurements as the probe falls. The AXBTs are part of the Oceans Melting Greenland (OMG) mission. In the fall of 2020 and 2021, the AXBT probes were used to supplement the ocean-per-year survey of ocean properties on the continental shelf surrounding Greenland. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.",
         "license": "proprietary"
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         "description": "This dataset contains in situ profile measurements from Airborne eXpendable Conductivity Temperature Depth (AXCTD) probes. It provides salinity, density, temperature and sound velocity as a function of depth in the water column. The AXCTDs were jettisoned from a plane to collect temperature and salinity readings around Greenland, where a ship would have had difficulties maneuvering. After landing in the water, the AXCTDs drop a weighted sensor from the surface that falls at a well-calibrated rate, measuring water temperature and conductivity as it falls.  An equation is used to determine the depth of the measurements as the probe falls, and another equation is used to convert temperature, depth and conductivity into salinity. These probes provided measurements of the ocean's physical characteristics around Greenland, where a ship would have had difficulties maneuvering. The AXCTDs are part of the Oceans Melting Greenland (OMG) mission. The AXCTDs were deployed in the fall from 2016 through 2021, covering the entire continental shelf surrounding Greenland as part of a once-per-year survey. The goal of the mission is to find out what contributions the ocean has on Greenland's melting glaciers.",
         "license": "proprietary"
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         "description": "This dataset contains level 2 in situ depth measurements from Multibeam Echo Sounder System (MBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The MBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions. Bathymetry was also measured with the Singlebeam Echo Sounder System (SBES) in areas where the MBES could not go, but has less spatial coverage.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772155-POCLOUD.html",
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         "description": "This dataset contains in situ depth measurements from Singlebeam Echo Sounder System (SBES) instruments. These depths were used to map the bathymetry around ocean terminating glaciers of Greenland. The bathymetry mapping is part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers. The SBES was onboard a ship so the tracks are not of a swath, but less regularly patterned as the ship is limited as to where it can traverse due to floating glaciers, ice cover and general weather conditions.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772156-POCLOUD.html",
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         "description": "This dataset contains in situ measurements from Conductivity Temperature Depth (CTD) casts and tows. It provides salinity, density, temperature and sound velocity of the water column. The CTDs were deployed from a ship either as single profile casts or towed yo-yo behind the ship to measure the physical properties of the water. This provided measurements of the ocean's physical characteristics around Greenland. The CTDs are part of the Oceans Melting Greenland (OMG) project. The goal of the project is to find out what contributions the ocean has on Greenland's melting glaciers.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772159-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772159-POCLOUD.html",
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         "description": "This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2837134642-POCLOUD.html",
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         "description": "This OMG Narwhals L3 dataset contains daily-averaged temperature and salinity measurements from CTD and temperature loggers from the same mooring.   <br><br>  NASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Between 2018 and 2020, five bottom-mounted moorings with marine mammal acoustic recorders and oceanographic instruments were deployed year-round near three glacier fronts: Sverdrup Glacier, Kong Oscar Glacier, and Rink Glacier.",
         "license": "proprietary"
     },
@@ -150561,7 +150223,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2837135414-POCLOUD.html",
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         "description": "This OMG Narwhals dataset contains measurements from the ship based full water column CTD profiles that were obtained during summer mooring deployment/recovery cruises.   <br><br>  NASA\u2019s Oceans Melting Greenland (OMG) campaign obtained oceanographic observations around Greenland at an unprecedented spatial scale and confirmed that the ocean plays a key role in Greenland glacier acceleration and retreat. Yet, ocean observations along Greenland\u2019s margins are biased toward summer months with relatively few year-round measurements. OMG Narwhals, a project coupled with NASA\u2019s OMG mission, seeks to understand the ecological importance of glacial habitats to narwhals. Narwhals return to glacial outlets and fjords each summer with high site fidelity but what attracts them to specific glacier fronts remains unclear. Seafloor-mounted ocean moorings with marine mammal acoustic recorders and oceanographic instruments were deployed near three glacier fronts with known narwhal presence in Melville Bay, northwest Greenland.",
         "license": "proprietary"
     },
@@ -151306,19 +150968,6 @@
         "description": "This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2.  OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the  retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags.  The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.",
         "license": "proprietary"
     },
-    {
-        "id": "OMSO2_003",
-        "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC",
-        "catalog": "GES_DISC STAC Catalog",
-        "state_date": "2004-10-01",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.html",
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-        "description": "The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources.  Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li.  The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.",
-        "license": "proprietary"
-    },
     {
         "id": "OMSO2_003",
         "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT",
@@ -151332,6 +150981,19 @@
         "description": "The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura  satellite on July 15, 2004 (1:38 pm equator crossing time, ascending  mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal  investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal  Netherlands Meteorological Institute). OMI is designed to monitor  stratospheric and tropospheric ozone, clouds, aerosols and smoke from  biomass burning, SO2 from volcanic eruptions, and key tropospheric  pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI  sensor counts, calibrated and geolocated radiances, and all derived  geophysical atmospheric products will be archived at the NASA Goddard  DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly  available from NASA GSFC Earth Sciences (GES) Data and Information  Services Center (DISC) for public access.  OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the  retrieval algorithm. It also contains quality flags, geolocation and other ancillary information.        The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu).       OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and   documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php )             For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/.       For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below.       http://disc.sci.gsfc.nasa.gov/Aura/       http://disc.gsfc.nasa.gov/acdisc/",
         "license": "proprietary"
     },
+    {
+        "id": "OMSO2_003",
+        "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC",
+        "catalog": "GES_DISC STAC Catalog",
+        "state_date": "2004-10-01",
+        "end_date": "",
+        "bbox": "-180, -90, 180, 90",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections?cursor=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%3D%3D/OMSO2_003",
+        "description": "The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources.  Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li.  The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.",
+        "license": "proprietary"
+    },
     {
         "id": "OMSO2_CPR_003",
         "title": "OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC",
@@ -151371,19 +151033,6 @@
         "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains  UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags.  The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.",
         "license": "proprietary"
     },
-    {
-        "id": "OMTO3_003",
-        "title": "OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT",
-        "catalog": "OMINRT STAC Catalog",
-        "state_date": "2004-07-15",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3_003",
-        "description": "The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA  for the public access.  The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that  essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/  http://www.knmi.nl/omi/research/documents/ .        Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .",
-        "license": "proprietary"
-    },
     {
         "id": "OMTO3_003",
         "title": "OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC",
@@ -151397,6 +151046,19 @@
         "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia.  The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.",
         "license": "proprietary"
     },
+    {
+        "id": "OMTO3_003",
+        "title": "OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT",
+        "catalog": "OMINRT STAC Catalog",
+        "state_date": "2004-07-15",
+        "end_date": "",
+        "bbox": "-180, -90, 180, 90",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3_003",
+        "description": "The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA  for the public access.  The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that  essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/  http://www.knmi.nl/omi/research/documents/ .        Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .",
+        "license": "proprietary"
+    },
     {
         "id": "OMTO3_CPR_003",
         "title": "OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution",
@@ -151423,19 +151085,6 @@
         "description": "The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids.  The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.",
         "license": "proprietary"
     },
-    {
-        "id": "OMTO3e_003",
-        "title": "OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC",
-        "catalog": "GES_DISC STAC Catalog",
-        "state_date": "2004-10-01",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections?cursor=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%3D%3D/OMTO3e_003",
-        "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles.  The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.",
-        "license": "proprietary"
-    },
     {
         "id": "OMTO3e_003",
         "title": "OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT",
@@ -151449,6 +151098,19 @@
         "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) .",
         "license": "proprietary"
     },
+    {
+        "id": "OMTO3e_003",
+        "title": "OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC",
+        "catalog": "GES_DISC STAC Catalog",
+        "state_date": "2004-10-01",
+        "end_date": "",
+        "bbox": "-180, -90, 180, 90",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections?cursor=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%3D%3D/OMTO3e_003",
+        "description": "The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles.  The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.",
+        "license": "proprietary"
+    },
     {
         "id": "OMUANC_004",
         "title": "Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUANC) at GES DISC",
@@ -151687,7 +151349,7 @@
         "id": "OPERA_L2_RTC-S1_V1_1",
         "title": "OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)",
         "catalog": "ASF STAC Catalog",
-        "state_date": "2023-10-04",
+        "state_date": "2021-01-01",
         "end_date": "",
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2777436413-ASF.umm_json",
@@ -151705,7 +151367,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746980408-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746980408-LPCLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibmFzYWRlbSBzcnRtIHN1YnN3YXRoIGdsb2JhbCAxIGFyYyBzZWNvbmQgdjAwMVwiLFwiTFBDTE9VRFwiLFwiTkFTQURFTV9TU1BcIixcIjFcIiwyNzYzMjY2MzI5LDE1XSIsInVtbSI6IltcIm5hc2FkZW0gc3J0bSBzdWJzd2F0aCBnbG9iYWwgMSBhcmMgc2Vjb25kIHYwMDFcIixcIkxQQ0xPVURcIixcIk5BU0FERU1fU1NQXCIsXCIxXCIsMjc2MzI2NjMyOSwxNV0ifQ%3D%3D/OPERA_L3_DIST-ALERT-HLS_V1_1",
+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibmFzYWRlbSBzcnRtIHN1YnN3YXRoIGdsb2JhbCAxIGFyYyBzZWNvbmQgdjAwMVwiLFwiTFBDTE9VRFwiLFwiTkFTQURFTV9TU1BcIixcIjFcIiwyNzYzMjY2MzI5LDE2XSIsInVtbSI6IltcIm5hc2FkZW0gc3J0bSBzdWJzd2F0aCBnbG9iYWwgMSBhcmMgc2Vjb25kIHYwMDFcIixcIkxQQ0xPVURcIixcIk5BU0FERU1fU1NQXCIsXCIxXCIsMjc2MzI2NjMyOSwxNl0ifQ%3D%3D/OPERA_L3_DIST-ALERT-HLS_V1_1",
         "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A and Sentinel-2B Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.  The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document  for a more detailed description of the individual layers provided in the DIST-ALERT product.  ",
         "license": "proprietary"
     },
@@ -151718,7 +151380,7 @@
         "bbox": "-180, -90, 180, 90",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2519119034-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2519119034-LPCLOUD.html",
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         "description": "The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS)  product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).  The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS).  See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product.   ",
         "license": "proprietary"
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         "description": "This dataset contains Level-3 Dynamic OPERA surface water extent product version 1.  The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km \u00d7 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.    To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2949811996-POCLOUD.html",
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         "description": "This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinal-1 (DSWx-S1) product version 1.  DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024.  Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.  To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov ",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036882482-POCLOUD.html",
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         "description": "This dataset consists of the version 2 Level 2B science-quality ocean surface wind vector retrievals from the Oceansat-2 scatterometer (OSCAT), which was designed and launched by the Indian Space Research Organization (ISRO) 23 September 2009. This Level 2B dataset is produced by the Jet Propulsion Laboratory (JPL) QuikSCAT Project in cooperation with ISRO. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. This resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. This newest version contains an improved geophysical model function (GMF), known as QSCAT12, consistent with the Remote Sensing Systems Ku2011 GMF, and an improved rain detection and flagging algorithm; these algorithms are consistent with the latest reprocessed version 3 QuikSCAT L2B dataset. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. As a Ku-band dual pencil-beam rotating scatterometer, OSCAT design specs bear a strong resemblence of the Ku-band SeaWinds scatterometers on QuikSCAT and Midori-II (ADEOS-II). The primary difference between OSCAT and SeaWinds is the ~4 degree increase in the OSCAT incidence angle, which acts as an offset to the relatively lower altitude of Oceansat-2 to help provide a nearly identical swath width to SeaWinds. In the early phase of cal/val, the JPL QuikSCAT Project identified several problems, most of which have been corrected in this latest L2B version. This dataset release is expected to have an accuracy similar to that of the version 3 L2B QuikSCAT product, with minor caveats, all of which are described by Jaruwatanadilok et al. (2014) and summarized in the User Guide document which is made available in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/docs/ . Read software is made available in MATLAB, Python, R, and IDL and is accessible in PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/oceansat2/L2B/oscat/jpl/sw/ .",
         "license": "proprietary"
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         "description": "OSCAR (Ocean Surface Current Analysis Real-time) contains near-surface ocean current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature data.  These input data sources are derived from various satellites and in situ instruments. The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient.  Data are on a 1 degree grid with a 5 day resolution.  OSCAR is generated by Earth Space Research (ESR) https://www.esr.org/research/oscar/oscar-surface-currents/",
         "license": "proprietary"
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         "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day.  OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/.  More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.",
         "license": "proprietary"
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         "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day.  OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/.  More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2102958977-POCLOUD.html",
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         "description": "Ocean Surface Current Analyses Real-time (OSCAR) is a global surface current database and NASA funded research project. OSCAR ocean mixed layer velocities are calculated from satellite-sensed sea surface height gradients, ocean vector winds, and sea surface temperature gradients using a simplified physical model for geostrophy, Ekman, and thermal wind dynamics. Daily averaged surface currents are provided on a global 0.25 x 0.25 degree grid as an average over an assumed well-mixed top 30 m of the ocean from 1993 to present day.  OSCAR currents are provided at three quality levels: final, interim and nrt with a respective latency of each of approximately 1 year, 1 month, and 2 days. OSCAR is generated by Earth & Space Research (ESR) https://www.esr.org/research/oscar/.  More details on the source datasets, file structure, and methodology can be found in oscarv2guide.pdf.",
         "license": "proprietary"
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         "description": "OSCAR (Ocean Surface Current Analysis Real-time) contains near-surface ocean current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature.  These data were collected from the various satellites and in situ instruments. The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient.  Data are on a 1/3 degree grid with a 5 day resolution.  OSCAR is generated by Earth Space Research (ESR) https://www.esr.org/research/oscar/oscar-surface-currents/. This collection contains data in 5-day files. For yearly files, see https://doi.org/10.5067/OSCAR-03D1Y",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2275018856-POCLOUD.html",
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         "description": "OSCAR (Ocean Surface Current Analysis Real-time) contains near-surface ocean current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature.  These data were collected from the various satellites and in situ instruments. The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient.  Data are on a 1/3 degree grid with a 5 day resolution.  OSCAR is generated by Earth Space Research (ESR) https://www.esr.org/research/oscar/oscar-surface-currents/. This collection contains data in yearly files. For 5-day files, see https://doi.org/10.5067/OSCAR-03D01",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2586786218-POCLOUD.html",
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         "description": "The Operational Sea Surface Temperature and Sea Ice Analysis Reprocessed (OSTIA-REP) is a  GHRSST reprocessed Level-4 sea surface temperature and ice-concentration analysis produced by the UK Met Office (UKMO) using optimal interpolation (OI) on a global 0.05 degree grid. It is a sister product of the Near Real Time version (OSTIA-NRT), but incorporates satellite data from over 25 different SST sensors as well as in situ data from drifting and moored buoys. The OSTIA-REP is produced on a biannual frequency when more satellite and climatology observations are available from existing geostationary IR, and polar orbiting IR and MW satellites in addition to the data used in OSTIA-NRT. <br><br>  While OSTIA-NRT is produced to mainly serve as a lower boundary condition in Numerical Weather Prediction (NWP) models, this OSTIA-REP aims to provide a more accurate and consistent record of SST measurements over time, which is crucial for detecting long-term climate trends and variability. Both versions follow GHRSST Data Processing Specification (GDS) version 2 format guidelines.<br><br>  Data to June 2022 are also distributed through the E.U. Copernicus Marine Service Information (https://marine.copernicus.eu/, DOI: https://doi.org/10.48670/moi-00168 with the following license). Please refer to the user guide for more information. ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036877535-POCLOUD.html",
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.05x0.05 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from over 10 unique sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) imager, the Infrared Atmospheric Sounding Interferometer (IASI), the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications and is updated daily with 24-hours nominal latency in a Near Real Time (NRT) mode. UKMO  also produces the higher quality reprocessed OSTIA L4 SST using more sensors and data with a biannual latency (https://podaac.jpl.nasa.gov/dataset/OSTIA-UKMO-L4-GLOB-REP-v2.0).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412511-MLHUB.html",
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         "description": "This dataset was developed as part of a challenge to segment building footprints from aerial imagery. The goal of the challenge was to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities [Read more about the [challenge](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)]. The data consists of drone imagery from 10 different cities and regions across Africa",
         "license": "proprietary"
     },
@@ -152411,1787 +152073,6 @@
         "description": "Measurements from the PACE Absorbance Closure project off the coast of Florida.",
         "license": "proprietary"
     },
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-        "id": "PACE_EPH_DEF_1",
-        "title": "PACE Definitive Ephemeris Data Data, V1",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "2024-02-25",
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-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3020918309-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3020918309-OB_CLOUD.html",
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-        "description": "The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. ",
-        "license": "proprietary"
-    },
-    {
-        "id": "PACE_HARP2_L0_1",
-        "title": "PACE HARP2 Level-0 Instrument Telemetry/Multi-Detector Data, V1",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "2024-02-25",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798249-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2804798249-OB_CLOUD.html",
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-        "description": "The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.",
-        "license": "proprietary"
-    },
-    {
-        "id": "PACE_HARP2_L0_D1_1",
-        "title": "PACE HARP2 Level-0 Detector 1 (D1) Data, V1",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "2024-02-25",
-        "end_date": "",
-        "bbox": "-180, -90, 180, 90",
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         "id": "PAC_DFO_PAR_BUOY_0",
         "title": "PAR data from buoys along the west coast of Canada",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Global Lake/Reservoir Surface Inland Water Extent Mask Time Series are derived from the MODIS instruments. The purpose of this dataset is to provide surface water dynamics for several hundred lakes and reservoirs throughout the globe, with a base temporal resolution of 8 days and a spatial resolution of 500 meters. With the exception of periods of low-quality input data, these time series will extend across the lifespan of the MODIS multispectral reflectance products, from roughly 2000 to present. These time series will allow us to satisfy the project goal to produce ESDRs of suitable quality to support long-term trend analysis and global water dynamics models for the longest length possible (in most cases, about 20 years, the length of the altimetry record) of key measures of surface water storages and fluxes. This product should be accessible and of direct use to both water managers and the scientific community worldwide, and will allow for improved assessment and modeling of human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.",
         "license": "proprietary"
     },
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         "description": "The Global Lake/Reservoir Storage Time Series is derived from the Surface Water Height Time Series and Surface Water Extent Mask Time Series products. The purpose of this dataset is to provide surface water storage estimates for several hundred lakes and reservoirs across the globe. These time series potentially span a 25 year time period, from late 1992 to 2017, satisfying the project goal of ESDR creation with a suitable level of quality that supports long-term trend analysis and global water dynamics models. This product is readily accessible and is of direct use to both water managers and the scientific community worldwide, and allows for improved assessment and modeling of the human impact on the global water cycle. These pre SWOT data are derived from satellites to provide hydrological measurements. The Surface Water and Ocean Topography (SWOT) mission will have hydrology as one of its objectives. This dataset does not have the same variables as SWOT, but does provide hydrological measurements with typical quality flagging typical of satellite data. Not only does it provide science information, it can also assist hydrological users new to satellite data with the satellite data formats and variables before SWOT launches.",
         "license": "proprietary"
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         "description": "This is the PI-produced SMAP sea water salinity, level 2 v1.0 orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Parameterized Rain Impact Model (PRIM) developed at the University of Central Florida (UCF) Central Florida Remote Sensing Lab (CFRSL), Orlando, FL; University of Washington (UW) Applied Physics Lab (APL), Seattle, WA. <br><br>  The PRIM product range extended from March 31, 2015 to September 30, 2021. It includes data for a range of parameters: derived SMAP sea water salinity at surface, 1m depth and 5m depth, and probability of salinity stratification (PSS), rainfall rate and wind speed data. Each data file covers one 98-minute orbit (15 files per day), and corresponds to a <a href=\"https://doi.org/10.5067/SMP50-2TOCS\">JPL SMAP Level 2B CAP Sea Surface Salinity V5.0</a> file which corresponds to a single orbit on a given day. <br><br>  The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412418-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412418-MLHUB.html",
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         "description": "This dataset contains field boundaries and crop type information for fields in Kenya. PlantVillage app is used to collect multiple points around each field and collectors have access to basemap imagery in the app during data collection. They use the basemap as a guide in collecting and verifying the points. <br><br> Post ground data collection, Radiant Earth Foundation conducted a quality control of the polygons using Sentinel-2 imagery of the growing season as well as Google basemap imagery. Two actions were taken on the data 1)several polygons that had overlapping areas with different crop labels were removed, 2) invalid polygons where multiple points were collected in corners of the field (within a distance of less than 0.5m) and the overall shape was not convex, were corrected. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item). ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491225288-POCLOUD.html",
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         "description": "This dataset contains the first provisional release of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the QuikSCAT Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These ancillary files include: i) ocean surface wind fields from ERA-5 short-term forecasts (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobeCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. The primary purpose of this release is for provisional evaluation to be provided by the NASA International Ocean Vector Winds Science Team (IOVWST). As such, this release is not intended for science-quality research, and is subject to future revision based on feedback provided by the IOVWST. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 QuikSCAT sampled locations at 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617177020-POCLOUD.html",
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         "description": "This dataset is Version 2 of the geo-located and averaged Level 1B Sigma-0 measurements and wind retrievals from the SeaWinds on QuikSCAT platform, initiated in the months following the failure of the rotating antenna motor on 22 November 2009, using the various incidence angles at which QuikSCAT was pointed during the time period from November 2009 until present. Incidence angles were varied in order to cross-calibrate the Oceansat-2 and RapidScat scatterometers and to extend the known Ku-band geophysical model function. The averaging of the L1B input data combined with the wind vector processing results are both contained in this product referred to hereafter as Level 1C (L1C). The fixed and repointed beam processing is relative to either the one corresponding to the vertically polarized \"outer\" beam or the other corresponding to the horizontally polarized \"inner\" beam. The Sigma-0 values from the fixed operating beam for each frame are averaged to a single value representing approximately 50 samples. The data points are land flagged, collocated with ECMWF surface winds, and have climatological nadir attenuations provided for the location and time of the data (not applied to the sigma0). The following enhancements have been applied in the Version 2 re-processing: 1) the GMF has been updated (QNS2016a) to make use of ECMWF nowcast 1x1 degree resolution wind direction information for the entire historical data record; 2) the new QNS2016a GMF leverages a calibration adjustment from Remote Sensing Systems (RSS) resulting in a consistently lower Normalized Radar Cross Section (NRCS or Sigma-0) measurements that establishes a Sigma-0 bias of -0.25 dB (-5.9% linear scale) compared to the L1C Version 1 data; 3) the new QNS2016a GMF also applies an azimuthal modulation that is decreased by several tenths of a dB (for Sigma-0) in variation with wind speed; this results in a more consistent wind speed retrieval comparison between \"non-spinning\" and \"spinning\" modes of the QuikSCAT instrument; 4) spacecraft attitude was re-estimated using slice data over multiple orbits as a replacement for lost echo-tracking capability during the \"non-spinning\" mode of the instrument; this new attitude estimation follows an unpublished manual technique that leverages the echo power of individual slice observations; since only a small subset of slice observations are analyzed, rapid variations in attitude are not captured; 5) continues data production beyond October 2016 through the end of mission on 30 August 2018. Retrieved wind directions are only slightly different from ECMWF values and should not be considered an independent measurement of wind direction. Retrieved wind speeds do not depend significantly on ECMWF speeds as evidenced by the fact that they agree closely with WindSAT polarimetric radiometer speeds whenever WindSAT and ECMWF disagree. The Sigma0 values have also been corrected for scan loss (due to the fact that the antenna does not scan) and for X-factor changes due to repointing.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2695614586-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2695614586-POCLOUD.html",
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         "description": "The SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse  \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. This dataset represents the second reprocessed version of the Level 1B release. Special note: QuikSCAT went into a \"non-spinning\" mode on 22 November 2009. The final rev number in the nominal Operational \"spinning\" mode is 54296; the \"non-spinning\" mode of the instrument continued predominantly until the end of the time series. There were some brief periods of \"spinning\" in between, which include the following days and rev numbers (identified in parenthesis): 1) 29 January 2013 to 5 February 2013 (7909-71011), 2) 14 March 2013 (71536-71549), 3) 18 March 2013 to 21 March 2013 (71590-71634), and 4) 28 March 2013 to 31 March 2013 (71735-71769). Data during the \"non-spinning\" mode is not consistently calibrated with data from the \"spinning\" mode. Furthermore, incidence angles change periodically during the \"non-spinning\" mode. It is therefore advised that only \"expert\" users attempt using the data during the \"non-spinning\" mode. For standard L1B data users who wish to access consistently calibrated L1B data during the \"non-spinning\" mode, please consider using the L1B Averaged Sigma-0 dataset as alternative, which may be accessed by contacting podaac@podaac.jpl.nasa.gov",
         "license": "proprietary"
     },
@@ -156229,7 +154110,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2526576230-POCLOUD.html",
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         "description": "This dataset contains the latest reprocessed version 3 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3 processing begins with the same L1B (time-ordered backscatter) data as used in the previous processing. Version 3 has several improvements over the previous JPL processing of the QuikSCAT L2B winds: 1) changes to measurement binning, which was done in order to decrease noise and reduce gaps in the 12.5 km L2B wind retrievals, 2) an improved geophysical model function (GMF) to model the effect of wind on backscatter, 3) a neural network approach to correct rain contaminated winds speeds, 4) cross-track dependent wind speed biases were estimated and removed from the wind retrievals. The 12.5 km binning resolution enables users to obtain wind vector retrievals 10 km closer to shore when compared to the 25 km L2B dataset (only available in versions 1 and 2). More details to the processing changes and improvements are noted by Fore et al. (2014): PO.DAAC Drive at https://podaac-tools.jpl.nasa.gov/drive/files/allData/quikscat/L2B12/docs/fore_et_al_ieee_2014.pdf . Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). The Version 3 User Guide document is accessible from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/quikscat/open/L2B12/docs/qscat_l2b_v3_ug_v1_0.pdf.",
         "license": "proprietary"
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         "description": "This dataset contains the latest reprocessed version 4.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is an official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). Version 4.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 4.0 processing. This new version has a number of key improvements and changes over the previous version 4.0, including: 1) winds are now retrieved to within 5-km and 10-km of the coast within oceans/seas and lakes respectively; 2) coastal winds are now flagged as poor coastal quality and likely corrupted in orbits with estimated spacecraft pitch error greater than 0.04 degrees, which affects 150 orbits of data where coastal winds are severely contaminated by land due to poor attitude knowledge (note: attitude error tracking can identify pitch error but not yaw error, so when estimated pitch error is far from zero, it implies yaw error is large and uncorrected); 3) coastal winds are flagged based upon the long term mean wind speed and standard deviation of wind speed for each place on the ground; 4) four quantities, means and standard deviations computed with and without the land contamination correction algorithm applied (note: higher mean and smaller standard deviation are evidence of land contamination), are used to estimate the expected wind speed bias with respect to neighboring wind vector cells over open water; 5) wind vector cells with estimated speed bias greater than 0.4 m/s are flagged as poor coastal quality and likely corrupted; 6) winds within 5-km of the coast of an ocean/sea and 10-km of the coast of a lake are flagged as poor coastal quality and likely corrupted; the larger distance threshold for lakes is due to higher variability in lake water levels.",
         "license": "proprietary"
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         "description": "This dataset contains the latest reprocessed version 3.1 of the Level 2B science-quality ocean surface wind vector retrievals from the QuikSCAT scatterometer. The retrievals are provided on a non-uniform grid within the swath at 12.5 km pixel resolution. Higher resolution is achieved through a slice composite technique in which high resolution slice measurements from L1B data are composited into a 12.5 km wind vector cell. Version 3.1 processing begins with the same L1B (time-ordered backscatter) data as used in the previous Version 3.0 processing. Version 3.1 improves upon the previous Version 3.0 processing by incorporating enhanced coastal processing using a Land Contamination Ratio (LCR) method with a fixed threshold. The 12.5 km binning resolution combined with the LCR processing enables this dataset to provide wind vector retrievals with approximately half the coastal gap as compared to the Version 3.0 12.5 km L2B dataset. The geophysical model function used to produce the wind vector cell retrievals remains unchanged between Version 3.0 and 3.1. Each L2B file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit. This is the official dataset produced by the NASA QuikSCAT Project through the SeaWinds Processing and Analysis Center (SeaPAC). More details to the processing changes and improvements are to be published in the near future, but for now can be referenced by the following presentation: https://mdc.coaps.fsu.edu/scatterometry/meeting/docs/2016/Thu_AM/coastal-poster.pdf .",
         "license": "proprietary"
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         "description": "This dataset contains the first provisional release of the MEaSUREs-funded Earth Science Data Record (ESDR) of inter-calibrated ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from QuikSCAT scatterometer observations. The primary purpose of this release is for provisional evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). As such, this release is not intended for science-quality research, and is subject to future revision based on feedback provided by the IOVWST. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2  (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis.  The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects.  Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis.  In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772104-POCLOUD.html",
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         "description": "The Reconstructed Sea Level dataset contains sea level anomalies derived from satellite altimetry and tide gauges.  The satellite altimetric record provides accurate measurements of sea level with near-global coverage, but it has a relatively short time span, since 1993. Tide gauges have measured sea level over the last 200 years, with some records extending back to 1807, but they only provide regional coverage, not global.  Combining satellite altimetry with tide gauges, using a technique known as sea level reconstruction, results in a dataset with the record length of the tide gauges and the near-global coverage of satellite altimetry.  Cyclostationary empirical orthogonal functions (CSEOFs), derived from satellite altimetry, are combined with historical sea level measurements from tide gauges to create the Reconstructed Sea Level dataset spanning from 1950 through 2009. Combining the altimetric and tide gauge records alleviates the difficulties caused by the short record length and poor spatial distribution of the satellite altimetry and tide gauges, respectively.  Previous sea level reconstructions have utilized empirical orthogonal functions (EOFs) as basis functions, but by using CSEOFs and by addressing other aspects of the reconstruction procedure, an alternative sea level reconstruction can be computed. The resulting reconstructed sea level dataset has weekly temporal resolution and half-degree spatial resolution.  For specific information on the algorithm and how the CSEOFs are applied to the tide gauge data please see Hamlington et al. 2011.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis by the Oceanographic Modeling and Observation Network (REMO) at Applied Meteorology Laboratory/Federal University of Rio de Janeiro (LMA/UFRJ) using the Barnes sub optimal interpolation (OI) technique on a regional 0.05 degree grid. REMO uses Advanced Very High Resolution Radiometer (AVHRR) data from National Oceanic and Atmospheric Administration (NOAA) satellites series (NOAA 15, NOAA 16, NOAA 17, NOAA 18 and NOAA 19) and Microwave Imager (TMI) data from Tropical Rainfall Measuring Mission (TRMM) which is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) to generate 0.05 degree daily cloud free blended (infrared and microwave) SST products (approximately 5.5 km). The data lies between latitudes 45 S and 15 N and longitudes 70 W and 15 W region and are fully validated by in situ measurements from eleven buoys of Prediction and Research Moored Array in the Tropical Atlantic (PIRATA).AVHRR is a scanning radiometer capable of detecting energy from land, ocean and atmosphere. It operates with six spectral bands arranged in the regions of visible and infrared region. TRMM was launched in December, 1997, having an orbital inclination of 53 degree and altitude 350 km, an equatorial orbit that ranges from 40 N to 40 S and a spatial resolution of 0.25 degree (&#8764;27.75 km). Although infrared AVHRR SST data have high spatial resolution, they are contaminated by cloud cover and aerosols, while lower resolution microvwave TMI data are barely influenced by these.",
         "license": "proprietary"
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         "description": "The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 5 of this dataset implements release 3.0 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) system data.  As for the prior ERSST version, v5 implements Empirical Orthogonal Teleconnection analysis (EOT) but with an improved tuning method for sparsely sampled regions and periods. ERSST anomalies are computed with respect to a 1971-2000 monthly climatology.  The version 5 has been improved from previous version 4. Major improvements in v5 include: 1) Inclusion and use of new sources and new versions of input datasets, such as data from Argo floats (new source), ICOADS R3.0 (from R2.5), HadISST2 (from HadISST1) sea ice concentration, and 2) Improved methodologies, such as inclusion of additional statistical modes, less spatial-temporal smoothing, better quality control method, and bias correction with baseline to modern buoy observations. The new version improves the spatial structures and magnitudes of El Nino and La Nina events. The ERSST v5 in netCDF format contains extended reconstructed sea surface temperature, SST anomaly, and associated estimated SST error standard deviation fields, in compliance with CF1.6 standard metadata.",
         "license": "proprietary"
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         "description": "The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.<br><br>    This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.    Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.",
         "license": "proprietary"
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         "description": "This dataset contains the multi-sourced microwave radiometer wind speed, rain and cloud liquid water data collocated to RapidScat Level 2B wind vector cell (WVC) locations. The corresponding NASA mission is officially referred to as ISS-RapidScat. This dataset is produced by Remote Sensing Systems (RSS) with direct funding from the JPL RapidScat project. All of the collocated radiometer data is produced by RSS. The co-located radiometer sources include: 1) DMSP SSM/I (F15) and SSMIS (F16/F17), 2) Coriolis WindSat, 3) GCOM-W1 AMSR2 and 4) GPM Core GMI; more details on these radiometer sources and sensors can be extracted by scrolling down to the \"Platform/Sensor\" section below this description. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-4 file format that follows the netCDF \"classic\" model and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.",
         "license": "proprietary"
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         "description": "This dataset contains the ISS-RapidScat Version 2.0 Level 1B geo-located Sigma-0 measurements and antenna pulse \"egg\" and \"slice\" geometries as derived from ephemeris and the Level 1A dataset. The pulse \"egg\" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. Version 2.0 represents a complete historical re-processing of the L1B data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). The Version 2.0 is also the dataset used to derive the Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. This dataset is intended for expert use only. If you must use RapidScat Sigma-0 data but you are unsure about how to use the L1B data record, please consider using either of the following L2A datasets: 1) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_25KM_V2.0 or 2) https://podaac.jpl.nasa.gov/dataset/RSCAT_L2A_12KM_V2.0. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the ISS Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. ",
         "license": "proprietary"
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         "description": "This dataset contains the Version 2.0 ISS-RapidScat on Level 2A 12.5 km science data record, which provides surface-flagged sigma-0 in 12.5 km Wind Vector Cells processed using the pulse \"slice\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the RapidScat instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.",
         "license": "proprietary"
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         "description": "This dataset contains the Version 2.0 ISS-RapidScat Level 2A 25km science data record, which provides surface-flagged sigma-0 in 25km Wind Vector Cells processed using the pulse \"egg\" Sigma-0 data provided by the Level 1B dataset. Due to the circular scan of the SeaWinds instrument the expected number of Sigma-0 cells per WVC is not constant. To minimize the L2A data volume, the Sigma-0 cell data are stored as \"lists\" for each WVC row, with each list indexed by a \"cell_index\" array to indicate the cross-track WVC membership of the data. Each cell is then checked for land or ice and flagged accordingly. Attenuation corrections for each Sigma-0 measurement are also provided. Version 2.0 represents a complete historical re-processing of the L2A data record and provides a calibration which is consistent across the several signal to noise ratio states experienced by RapidScat throughout its operation period (see the technical note for Version 2.0 under Documentation). It is also derived from the same L1B V2.0 product that was used to generate Version 2.0 wind products (L2B). Data are provided in single-orbit files in HDF-4 format. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval.",
         "license": "proprietary"
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         "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via Direct Download and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This climate quality data set differs from the nominal \"slice\" L2B dataset as follows: 1) it uses full antenna footprint measurements (~20-km) without subdividing by range (~7-km) and 2) the absolute calibration has been modified for the two different low signal-to-noise ratio (SNR) mode data sets: LowSNR1 14 August 2015 to 18 September 2015; LowSNR2 6 October 2015 to 7 February 2016. The above enhancements allow this dataset to provide consistent calibration across all SNR states. Low SNR periods and other key quality control (QC) issues are tracked and kept up-to-date in PO.DAAC Drive at https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/rapidscat/open/L1B/docs/revtime.csv. If you have any questions, please visit our user forums: https://podaac.jpl.nasa.gov/forum/.",
         "license": "proprietary"
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         "description": "This dataset contains the RapidScat Level 2B 12.5km Version 2.0 Climate quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the using the \"full aperture\" normalized radar cross-section (NRCS, a.k.a. Sigma-0) from the L1B dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. The new version has two important improvements over the previous version 1.0. First, an SST-dependent GMF developed by Lucrezia Ricciardulli of Remote Sensing Systems is used in wind retrieval in order to fix persistent speed biases in Ku-band data over cold ocean.  Second, flagging is simplified and extra flags are provided. All the previously existing flags are still there and still reflect the same meaning and purpose. A new single bit wind_retrieval_likely_corrupted_flag specifies the approximately 3% of the data which is known to have suboptimal performance due to rain, ice, or a few other rare anomalous cases. Another bit wind_retrieval_possibly_corrupted_flag specifies the approximately 15% of the data near rain, near ice, or near the coast, that is thought to be high quality but may not match up well with numerical wind models due to either remaining rain/ice/land contamination or variability in the winds near ice, rain, and coasts that are not reflected in the NWPs.  In addition to these two new bits, copious quality information is provided in the data to allow users to tailor flags to meet their own needs. There is also an added a global attribute called rev_status that specifies whether the RapidScat Instrument was in the original (highest data quality) high SNR mode, or one of the four low SNR time periods, the latter of which indicates the accuracy of winds below 5 m/s is degraded. This attribute also serves to identify MARGINAL orbits in which there are large gaps in the data record due to suboptimal spacecraft attitude. Other than gaps in the data, the accuracy of the winds in the MARGINAL orbits are similar to other orbits. This dataset is provided in netCDF-4 format and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above.",
         "license": "proprietary"
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         "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.1 science-quality ocean surface wind vectors. The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.1 dataset differs from the previous Version 1 dataset as follows: 1) A new neural network approach for high wind speeds provided rain corrections for the \"retrieve_wind_speed\" variable for wind speeds in excess of 15 m/s. 2) The data variables containing the number of measurements of each type for each wind vector cell have been corrected; these variables include \"number_in_aft\", \"number_in_fore\", \"number_out_aft\", and \"number_out_fore\". 3) The \"wind_obj\" data variable has been corrected to include the proper data for the conditional probability for the objective DIRTH function values. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions, please contact podaac@podaac.jpl.nasa.gov",
         "license": "proprietary"
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         "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.",
         "license": "proprietary"
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         "description": "This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data.  The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the \"Data Access\" tab above. It is advised for users to avoid using the \"wind_obj\" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the \"ambiguity_obj\" variable. The \"wind_obj\" variable contains DIRTH probabilities (which are derived form the \"ambiguity_obj\" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/.",
         "license": "proprietary"
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         "description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412768-MLHUB.html",
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         "description": "This dataset contains field boundaries for smallholder farms in eastern Rwanda. The Nasa Harvest program funded a team of annotators from TaQadam to label Planet imagery for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet's NICFI program. Planet's basemap imagery is provided for six months (March, April, August, October, November and December). The paired dataset is provided in 256x256 chips for a total of 70 tiles covering 1532 individual fields.<br><br>Input imagery consists of a time series of planet Basemaps from the NICFI program (monthly composite) data.<br><br>Imagery Copyright 2021 Planet Labs Inc. All use subject to the Participant License Agreement.",
         "license": "proprietary"
     },
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-        "title": "Sentinel-2/MSI - Level-2A - Data Cube - LCF 16 days",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204485-INPE.umm_json",
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-        "description": "Earth Observation Data Cube generated from Copernicus Sentinel-2/MSI Level-2A product over Brazil. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid Version 2 (BDC_SM V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.",
-        "license": "proprietary"
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     {
         "id": "S2K_EACM_Subset_623_1",
         "title": "SAFARI 2000 Monthly Climatology for the 20th Century (New et al.)",
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         "description": "This is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5-degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.",
         "license": "proprietary"
     },
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-        "title": "Sentinel-2 - Level-1C",
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-        "description": "Copernicus Sentinel-2/MSI Level-1C product over Brazil. Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance images.",
-        "license": "proprietary"
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-        "title": "Sentinel-2 - Level-2A - Cloud Optimized GeoTIFF",
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-        "description": "Copernicus Sentinel-2/MSI Level-2A product over Brazil. Level-2A product provides orthorectified surface reflectance images (Bottom-Of-Atmosphere - BOA). This dataset is provided as Cloud Optimized GeoTIFF (COG).",
-        "license": "proprietary"
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     {
         "id": "S3A_OL_1_EFR_1",
         "title": "OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance",
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         "description": "The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2019 Saildrone Arctic campaign featured six Saildrone USVs (jointly funded by NOAA and NASA) deployed during a 150-day cruise in the Bering and Chukchi Seas, spanning from 14 May 2019 to 11 October 2019. The primary mission objective for 2019 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, which could lead to significant improvements in modeling of diurnal warming and understanding of the marginal ice zones. Additionally, these new data will provide additional Arctic SST observations to benefit SST algorithm development and validation, and for studies of air- sea-ice interactions. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2019_Arctic_Cruise_Report.pdf <p>  During the Arctic campaign, NASA-funded Saildrones SD-1036 and SD-1037 undertook transects in the Chukchi Sea, approaching the sea ice edge to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.<p>  The Saildrone Arctic 2019 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.   ",
         "license": "proprietary"
     },
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         "description": "The Saildrone Arctic 2021 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2021 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 76-day cruise in the Bering and Chukchi Seas, spanning from 6 July 2021 to 20 September 2021. The primary mission objective for 2021 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, with special emphasis on better understanding the spatial/temporal scales of air-sea covariance in the Chukchi Sea, which was accomplished by running a series of parallel tracks using the two Saildrones at varying horizontal offsets. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/2021_Saildrone_Arctic_Cruise_Report.pdf <p>  During the Arctic campaign, Saildrones SD-1057 and SD-1058 ran transects in the Chukchi Sea, approaching the sea ice edge (up to 50 km away) to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents.\\  The Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.  <p>  The Saildrone Arctic 2021 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses two netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.  ",
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         "description": "The Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2022 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 60-day cruise in the Bering and Chukchi Seas, spanning from 18 June 2022 to 17 August 2022. The primary mission objective for 2022 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, specifically in collaboration with the Distributed Biological Observatory (DBO; https://www.pmel.noaa.gov/dbo/; https://dbo.cbl.umces.edu/). Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2022_Arctic_Cruise_Report.pdf <p>  During the Arctic campaign, Saildrones SD-1041 and SD-1046 undertook distinct trajectories to cover designated areas. SD-1041 traversed repeat transects from Point Hope, AK southwestward to near the International Date Line, following DBO line #3 (https://dbo.cbl.umces.edu/images/Frey_DBOmap_IceEdge2022.png). In contrast, SD-1046 ventured northward to DBO line #4 and, upon sea ice retreat, proceeded further north to DBO line #5. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.  <p>  The Saildrone Arctic 2022 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.",
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         "description": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone ATOMIC (Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) campaign involved the deployment of a fleet of saildrones, jointly funded by NASA and NOAA, in the Atlantic waters offshore of Barbados over a 45 day period from 17 January to 2 March 2020. The goal was to understand the Ocean-Atmosphere interaction particularly over the mesoscale ocean eddies in that region. The saildrones were equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP).  Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone ATOMIC dataset is comprised of two data files for each of the three NASA-funded saildrones deployed, one for the surface observations and one for the ADCP measuements.  The surface data files contain saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) spanning the entire cruise at 1 minute temporal resolution. The ADCP files for each saildrone are at 5 minute resolution for the duration of the deployments. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.",
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         "description": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven.   The saildrone Baja campaign was a 60-day cruise from San Francisco Bay, down along the US/Mexico coast to Guadalupe Island and back again over the period 11 April 2018 to 11 June 2018.  Repeat surveys were taken around NDBC moored buoys, and during the final week of the cruise a targeted front was sampled.  Scientific objectives included studies of upwelling and frontal region dynamics, air-sea interactions, and diurnal warming effects, while its validation objectives included establishing the utility of data from the saildrone platform for assessment of satellite data accuracy and model assimilation.  During the Baja campaign, the single deployed saildrone was equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP).  Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface.  This Saildrone Baja dataset is comprised of one data file with the  saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution. A second file contains the ADCP current vector data that is depth-resolved to 100m at 2m intervals and binned temporally at 5 minute resolution.  All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Surface Wave Instrument Float with Tracking (SWIFT) drifter is a passive Lagrangian wave-following sensor platform. During the SASSIE deployment, five SWIFT drifters were deployed in September 2022, collecting measurements of salinity, sea surface temperature, waves, and meteorological data. SWIFT drifter buoys contain GPS, a pulse-coherent Doppler velocity profiler, an autonomous meteorological station, and a digital video recorder. Level 1 data are available as compressed files containing graphics of the measurements alongside MATLAB and NetCDF files.  ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. A waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During the SASSIE deployment, four wavegliders were deployed near Prudhoe Bay on 12-14 August 2022. The wavegliders collect measurements of ocean surface salinity, temperature, currents, waves, and meteorological data. Custom integrated Casting CTDs provide additional profiles of salinity and temperature to a depth of 150m below the surface. L1 data are available as a compressed file containing graphics of the measurements alongside MATLAB data files. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge.  This dataset contains temperature and salinity measurements collected by ALTO and Air Launched Autonomous Micro Observer (ALAMO) profiling floats deployed in the Beaufort Sea. ALTO floats had ice-avoidance firmware, meaning that they stopped surfacing and transmitting data once surface temperatures dropped to near-freezing values (indicating the presence of sea ice). They will hopefully reappear in summer 2023 to report data from the previous ice-covered season. ALAMO floats did not have ice-avoidance, in order to ensure that they reported data as long as possible during ice freeze-up. As a result, they will likely not survive over the winter. Future versions if this dataset may include data collected after Fall 2022. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge.  This dataset contains ocean temperature and salinity data collected by surface drifting buoys (called UpTempO or Hydrobuoys, interchangeably) deployed in the Beaufort Sea.  Each buoy has a different configuration of sensors, and records to a maximum of 60 m depth. Drifters were left at sea after the completion of the field deployment and are recording data into March 2023. Future versions of this dataset will provide updated data with final quality control and additional parameters. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge.  This dataset contains ocean temperature and salinity data collected by surface drifting buoys (called UpTempO or Hydrobuoys, interchangeably) deployed in the Beaufort Sea.  Each buoy has a different configuration of sensors, and records to a maximum of 60 m depth. Drifters were left at sea after the completion of the field deployment and are recording data into March 2023. Version 2p data has major quality control performed. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. This dataset contains near-surface air pressure, temperature, and winds, as well as ocean temperature and salinity measurements collected using a Jet Surface Salinity Profiler (Jet-SSP). The Jet-SSP is a remotely operated kayak containing various instrumentation. It moved along various horizontal trajectories each deployment, traveling up to 5 kts as it collected data. Data are available in NetCDF format. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. The Passive-Active L-Band System (PALS) is an airborne microwave radiometer that senses ocean temperature and surface wind speed. The brightness temperature data is obtained at 1.4GHz using the PALS conical scanner, with the raw data sampled at 1ms and gridded over an approximate 2x2km grid. Several quality control steps were done to remove any scan dependent biases, radio frequency interference, wind-speed dependencies. Calibration of the sensor was done with respect to special aircraft maneuvers as well as in-situ samples. Brightness temperature were converted to salinity via the Klein-Swift salinity retrieval model. Data is available in netCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains ice concentration rankings of S-band images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Ice concentration rankings ranging from 0 to 3 determined by VGG19 Machine Learning Model. L2 summary data are available in NetCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains measurements of shipboard ocean current speed vertical shear from an acoustic doppler current profiler (ADCP) during the Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge.",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge.  This dataset contains ocean temperature, salinity, and acoustic range measurements collected by an autonomous under ice float deployed in the Beaufort Sea from September 10, 2022 to October 22, 2022.  Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "The Salinity and Stratification at the Sea Ice Edge (SASSIE) project is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200 km of the sea ice edge. A waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During the SASSIE deployment, four wavegliders were deployed near Prudhoe Bay on 12-14 August 2022. The wavegliders collect measurements of ocean surface salinity, temperature, currents, waves, and meteorological data. Custom integrated Casting CTDs provide additional profiles of salinity and temperature to a depth of 150m below the surface. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "This dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Images were then georeferenced based on range, heading, image orientation, and ship's GPS position. L3 images are available in GeoTIFF format.",
         "license": "proprietary"
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         "description": "This dataset contains images from the S-BAND marine navigation radar collected during the 2022 Salinity and Stratification at the Sea Ice Edge (SASSIE) campaign. SASSIE is a NASA experiment that aims to understand how salinity anomalies in the upper ocean generated by melting sea ice affect sea surface temperature (SST), stratification, and subsequent sea-ice growth. SASSIE involved a field campaign that sampled the transition from summer melt to autumn ice advance in the Beaufort Sea during August-October 2022, making intensive in situ and remote sensing observations within ~200km of the sea ice edge. Images from the radar were digitized and saved every 10-60 seconds on days that the R/V Wolstad was in or around ice. Images were then georeferenced based on range, heading, image orientation, and ship's GPS position. L4 images are available in GeoTIFF format and contain additional processing to classify pixels as sea ice, not sea ice, or not data.",
         "license": "proprietary"
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         "description": "This dataset contains the first science quality release (post-provisional after v1.0) of the MEaSUREs-funded Earth Science Data Record (ESDR) of ancillary data corresponding to the SCATSAT-1 Level 2 (L2) data products, interpolated in space and time to the scatterometer observations. These ancillary files include: i) ocean surface wind fields from ERA-5 short-term forecast (removed from the analyses times to reduce impacts from assimilated scatterometer retrievals at the beginning of the forecast); ii) collocated in space and time estimations of precipitation from the GPM IMERG product; iii) estimation of the surface currents from the GlobCurrent project. These auxiliary fields are included to complement the scatterometer observation fields and to help in the evaluation process. The modeled ocean surface auxiliary fields are provided on a non-uniform grid within the native L2 SCATSAT-1 sampled locations at a nominal 12.5 km pixel resolution. Each file corresponds to a specific orbital revolution (rev) number, which begins at the southernmost point of the ascending orbit.   <br><br>  The dataset represents the first science quality release of this product with funding from the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) improved variable metadata, 2) removed the GlobCurrent stokes drift variables, and 3) provided data source metadata including DOIs for the ERA-5, IMERGE, and GlobCurrent data sources. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "description": "This dataset contains ocean surface wind vectors (equivalent neutral and true 10m) and wind stress vectors derived from satellite-based scatterometer observations aboard ScatSat-1, representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaAUREs program. This product from ScatSat-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites. The wind vector and stress retrievals are provided on a non-uniform grid within the swath (Level 2 (L2) products) at 12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. <br><br> The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. Version 1.1 provides a set of updates and improvements from version 1.0, including: 1) increased data coverage, 2) improved quality control, and 3) new global metadata attributes featuring revolution number, equator crossing longitude, and equator crossing time (UTC). The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST).",
         "license": "proprietary"
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         "description": "This dataset provides gridded Sea Surface Height Anomalies (SSHA) above a mean sea surface, on a 1/6th degree grid every 5 days. It contains the fully corrected heights, with a delay of up to 3 months. The gridded data are derived from the along-track SSHA data of TOPEX/Poseidon, Jason-1, Jason-2, Jason-3 and Jason-CS (Sentinel-6) as reference data from the level 2 along-track data found at https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_CYCLES_V51, plus ERS-1, ERS-2, Envisat, SARAL-AltiKa, CryoSat-2, Sentinel-3A, Sentinel-3B, depending on the date, from the RADS database. The date given in the grid files is the center of the 5-day window. The grids were produced from altimeter data using Kriging interpolation, which gives best linear prediction based upon prior knowledge of covariance.",
         "license": "proprietary"
     },
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         "description": "These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. This dataset is composed of co-registered optical and SAR images time series for the detection of flood events.",
         "license": "proprietary"
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         "description": "The SEN12TS dataset contains Sentinel-1, Sentinel-2, and labeled land cover image triplets over six agro-ecologically diverse areas of interest: California, Iowa, Catalonia, Ethiopia, Uganda, and Sumatra. Using the Descartes Labs geospatial analytics platform, 246,400 triplets are produced at 10m resolution over 31,398 256-by-256-pixel unique spatial tiles for a total size of 1.69 TB. The image triplets include radiometric terrain corrected synthetic aperture radar (SAR) backscatter measurements; interferometric synthetic aperture radar (InSAR) coherence and phase layers; local incidence angle and ground slope values; multispectral optical imagery; and decameter-resolution land cover data. Moreover, sensed imagery is available in timeseries: Within an image triplet, radar-derived imagery is collected at four timesteps 12 days apart. For the same spatial extent, up to 16 image triplets are available across the calendar year of 2020.<br><br>The SEN12TS documentation demonstrates two initial use cases for the dataset. The first transforms radar imagery into enhanced vegetation indices by means of a generative adversarial network, and the second tests combinations of input imagery for cropland classification.",
         "license": "proprietary"
     },
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         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG-3) satellites (launched 5 July 2012).     The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),  Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real  time from MSG/SEVIRI. SEVIRI level 1.5 data are acquired at Meteo-France/Centre de  Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved  from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm.  Atmospheric profiles of water vapor and temperature from a numerical weather prediction model,  together with a radiatiave transfer model, are used to correct the multispectral algorithm for  regional and seasonal biases due to changing atmospheric conditions. Every 15 minutes slot is  processed at full satellite resolution. The operational products are then produced by remapping  over a 0.05 degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all 15  minute SST data available in one hour time, and the priority being given to the value the closest in  time to the product nominal hour. The product format is compliant with the GHRSST Data  Specification (GDS) version 2.",
         "license": "proprietary"
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         "description": "A Group for High Resolution Sea Surface Temperature (GHRSST) dataset for the Eastern Atlantic Region from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the MSG satellites (Meteosat-8 and Meteosat-9). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) has reprocessed SST products in (long) delayed-mode from MSG/SEVIRI. SST is retrieved from the SEVIRI infrared channels (10.8 and 12.0 micrometer) using a multispectral algorithm and the cloud mask (CM) from OSI SAF. Atmospheric profiles of water vapor and temperature from a numerical weather prediction (NWP) model, OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. Every 15-minute slot is processed at full satellite resolution. The products are then produced by remapping over a 0.05-degree regular grid (60S-60N and 60W-60E) SST fields obtained by aggregating all available 15-minute SST data into hourly files with priority being given to the value closest in time to the product nominal hour. The product format is compliant with the GHRSST Data Specification (GDS) version 2.",
         "license": "proprietary"
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         "description": "The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument.  The SMAP instrument is in a near polar orbiting,  sun synchronous orbit with a nominal 8 day repeat cycle. <br><br>    The dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5)  CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom  April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval. <br><br>    This JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.",
         "license": "proprietary"
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@@ -161689,7 +159518,7 @@
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         "description": "The SMAP-SSS level 2C near real-time (NRT) V5.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument.  The SMAP instrument is in a near polar orbiting,  sun synchronous orbit with a nominal 8 day repeat cycle.   <br><br>  The dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. Data from July 28, 2022 to present, is available with a latency of about 6 hours. The observations are global, provided on a 0.25&deg; fixed Earth grid with an approximate spatial resolution of 70 km. The major differences to the standard version 5.0 data products are: (1) the NRT version of the L1B SMAP antenna temperatures is used, (2) the latest 6-hourly 0.25&deg; wind speed and direction are used for the ancillary wind speed and direction input, (3) the CMC SST from 2 days earlier is used for the ancillary SST input, (4) the sea-ice mask of the 3-day aggregate RSS AMSR-2 Air-Sea Essential Climate Variables (AS-ECV) data set from 2-days earlier is used for the sea-ice flag, (5) no correction for sea-ice contamination is performed, it is recommended to use only SMAP data that are classified to be within sea-ice zone 0 for open ocean scene and no sea-ice contamination.  <br><br>  This RSS SMAP-SSS V5.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
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         "description": "The SMAP-SSS level 2C near real-time (NRT) V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. SMAP, launched on January 31, 2015, was initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles, and has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument.  The SMAP instrument is in a near polar orbiting,  sun synchronous orbit with a nominal 8 day repeat cycle.   <br><br>  The dataset includes derived SMAP SSS, SSS uncertainty using the NRT SMAP Salinity Retrieval Algorithm, top of atmosphere brightness temperature (TB), wind speed and direction data for extreme winds, and other all necessary ancillary data and the results of all intermediate steps. The observations are global, provided on a 0.25&deg; fixed Earth grid with an approximate spatial resolution of 70 km. The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 5 hours l  atency.  <br><br>  This RSS SMAP-SSS V6.0 NRT dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature with its 1000km swath, it achieves global coverage in approximately three days, enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth\u2019s hydrological cycle, ocean circulation, and climate change.",
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         "description": "The version 4.0 SMAP-SSS, level 2C product contains the fourth release of the validated sea surface salinity orbital/swath data from the NASA Soil Moisture Active Passive (SMAP) observatory, and is produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include:  use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields  in a single data product. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day).  Data begins on April 1,2015 and is ongoing. Observations are global in extent and provided at a 0.25 degree x 0.25 degree grid with an approximate spatial feature resolution of 40KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data.The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days.  On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities.  Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
         "license": "proprietary"
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         "description": "The RSS SMAP level 2C  sea surface salinity V5.3 evaluation dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015  with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file covers one 98-minute orbit (15 files per day) and is available in netCDF-4 file format.   The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data.   Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
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         "description": "The version 5.0 SMAP-SSS, level 2C product contains the fourth release of the validated sea surface salinity orbital/swath data from the NASA Soil Moisture Active Passive (SMAP) observatory, and is produced operationally by Remote Sensing Systems (RSS). The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data. Each data file covers one 98-minute orbit (15 files per day).  Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days.  On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities.  Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
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         "description": "The RSS SMAP level 2C  sea surface salinity V6.0 dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015  with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag.  The SMAP-SSS L2C product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, brightness temperatures for each radiometer polarization, antenna temperatures, collocated wind speed, data and ancillary reference surface salinity data from HYCOM, rain rate, quality flags, and navigation data.   Each data file covers one 98-minute orbit (15 files per day), is available in netCDF-4 file format with about 4 days l  atency.  Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
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         "description": "The version 4.0 SMAP-SSS level 3, 8-Day running mean gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS). Enhancements with this release include:  use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields  in a single data product. Daily data files for this product are based on SSS averages spanning an 8-day moving time window.  SMAP data begins on April 1,2015 and is ongoing. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the  default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data.  The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days.  On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities.  Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
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         "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015  with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower  TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format.  Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
         "license": "proprietary"
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         "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015  with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag.  The RSS SMAP 8-Day running mean product is based on SSS averages spanning an 8-day moving time window, it includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM.   Each data file is available in netCDF-4 file format with about 7-day latency (after the end of the averaging period).  Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
         "license": "proprietary"
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         "description": "The version 4.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. Enhancements with this release include:  use of an improved 0.125 degree land correction table with land emission based on SMAP TB; replacement of the previous NCEP sea-ice mask with one based on RSS AMSR-2 and implementing a sea-ice threshold of 0.3% (gain weighted sea-ice fraction); revised solar flagging that depends on glint angle and wind speed; inclusion of estimated SSS-uncertainty; consolidation of both 40KM and 70KM SMAP-SSS datasets as variable fields  in a single data product. Monthly data files for this product are averages over one-month time intervals.  SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent and gridded at 0.25degree x 0.25degree with a default spatial feature resolution of approximately 70KM. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the  default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data.  The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days.  On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities.  Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.",
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         "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
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         "description": "The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.  <br><br>  The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km.",
         "license": "proprietary"
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         "description": "This data set includes tower-based Ka-band ocean surface backscatter measurements (cross section, incidence angle, radial velocity from radar, pulse-pair correlation) located offshore of Martha\u2019s Vineyard (41\u00b019.5\u2032N, 70\u00b034\u2032W), Massachusetts (USA) over a period of three months, from October 2019 to January 2020.  Data from the Ka-band radar are collected at multiple distances from the tower (up to ~32 m) at several incidence angles and at sub-second resolution. The measurements are provided as hourly files in netCDF format.   <br>  <br>  Ka-band backscatter data are often utilized to derived ocean surface vector winds. The instrument used for this dataset was a Ka-Band Ocean continuous wave Doppler Scatterometer (KaBODS) built by the University of Massachusetts, Amherst, which was installed on the Woods Hole Oceanographic Institution Air-Sea Interaction Tower (ASIT). The tower is located in 15 m deep water and extends 76 feet into the marine atmosphere. Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The measurements provided the opportunity to develop Ka-band backscatter models as well as study backscattering mechanisms under different wind, wave, and weather conditions in order to support operation of the airborne Ka-band Doppler scatterometer used during the main S-MODE intensive observation periods.  ",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2162104652-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2162104652-POCLOUD.html",
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         "description": "These wave slope data from polarimetry described below are considered preliminary and should not be used for any purpose without consulting Chris Zappa (zappa@ldeo.columbia.edu).  <br>  <br>  This data set includes tower-based measurements of ocean wave slope fields from visible-band polarimetry using a Polaris Pyxis Mono VIS polarimetric camera. The data here include wave slope fields at 30 frames per second temporal resolution and mm-scale spatial resolution over a ~2 m x 2 m area of ocean surface observed off the Air-Sea Interaction Tower (ASIT; 41\u00b020.1950'N, 70\u00b033.3865'W). Measurements were taken over the period from October 2019 through January 2020. Surface slopes are along two dimensions: along-look and cross-look orientations of the camera. Data was acquired for 10 minutes per hour, 8 hours per day, and each data file (netCDF-4) captures one of the 10-minute segments. Note that data files are large, 142 GB each.   <br>  <br>  Data were collected as part of a pre-pilot campaign for the S-MODE (Submesoscale Ocean Dynamics Experiment) project. The polarimetric slope sensing (PSS) technique of Zappa et al. [2008] allows one to reconstruct the water surface slope field by measuring the polarization state of reflected light at each image pixel, allowing for surface resolutions of order 1 mm with no in-water measurement component. From these data one is able to compute water surface slope variance, wave directional spreading, and the near-surface current profile. ",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2301076107-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2301076107-POCLOUD.html",
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         "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 1 data includes geolocated physical measurements for a measurement footprint, which are the basis for the DopplerScatt L2 surface winds and currents estimates. Data are available in netCDF format and are ordered by measurement acquisition time and radar range, and are not on a geospatial grid. ",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2793202077-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2793202077-POCLOUD.html",
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         "description": "This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available as GZIP flightlines containing individual TIFF images.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2431661343-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2431661343-POCLOUD.html",
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         "description": "This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available in TIFF format.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2431605582-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2431605582-POCLOUD.html",
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         "description": "This dataset contains airborne hyperspectral imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a hyperspectral camera operating in the visible to near-IR range (400-990 nm). Hyperspectral data are used by S-MODE to provide visible imagery of the kinematics of whitecaps and ocean color measurements. Level 1 data are available as zip files containing data in ENVI format and text files containing location and timing information.",
         "license": "proprietary"
     },
@@ -162209,7 +160038,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2110184918-POCLOUD.html",
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         "description": "This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30\u00b0 cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.",
         "license": "proprietary"
     },
@@ -162222,7 +160051,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2431645288-POCLOUD.umm_json",
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         "description": "NOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available.  <br>  This dataset contains airborne longwave infrared (LWIR) imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a FLIR SC6700 camera with 13mm lens was mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the flight track. The camera was synchronized to a coupled GPS/IMU system with images collected at 50hz. Raw images were calibrated for lens distortion, vignetting, and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to the WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent resolution. Level 1 images are available in TIFF format.",
         "license": "proprietary"
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         "description": "This dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.",
         "license": "proprietary"
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         "description": "This dataset contains PRISM data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which will produce high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and will be used to provide accurate atmospheric correction of the ocean color measurements. Level 1 data is available in netCDF format. ",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2574191901-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2574191901-POCLOUD.html",
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         "description": "This dataset contains a suite of Saildrone in-situ measurements (including but not limited to temperature, salinity, currents, biochemistry, and meteorology) taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign spanning two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples are available in their raw 1 Hz sampling frequency as well as 5 minute averages, the latter available with navigation data. Other measurements are available as raw files (1Hz or 20 Hz where applicable), as well as 1 minute averages. L1 data are available as a zip file.  ",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2864321540-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2864321540-POCLOUD.html",
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         "description": "This dataset contains APEX float in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. Data was collected approximately 300 km offshore of San Francisco, during Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVO) APEX floats measure subsurface properties including temperature and salinity. Data are available in netCDF format.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2110184925-POCLOUD.html",
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         "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V1 data have been cross-calibrated against SIO-DopVis leading to the 'dopvis_2021' current geophysical model function. It is expected that additional DopVis data will lead to a reprocessing of this data set and it should be regarded as provisional, to be refined after future S-MODE deployments. Data are available in netCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023.  DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V2 data have been cross-calibrated against ADCPs, surface drifters, and the SIO-DopVis instrument collected during the Pilot and IOP1 campaigns. Additional DopVis data collected during IOP1 and IOP2, in addition to IOP2 ADCP and surface drifter data will lead to a reprocessing of this dataset, and it should be regarded as provisional. Data are available in netCDF format.",
         "license": "proprietary"
     },
@@ -162313,7 +160142,7 @@
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         "description": "This dataset contains in-situ position data from surface drifters from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Drifting buoys were deployed from the research vessels and configured to nominally report positions every five minutes. Drifter position as a function of time is used by S-MODE to measure the average horizontal velocity of currents in the upper 60 cm of the ocean. Tracking and telemetry of the drifters is done by Pacific Gyre, Inc. The data are available in netCDF format with a dimension of time. ",
         "license": "proprietary"
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         "description": "This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity. ",
         "license": "proprietary"
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         "description": "This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments.  Data are available in netCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains estimated chlorophyll-a and particulate organic carbon concentration data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2 chlorophyll-a data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "This dataset contains Saildrone in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Saildrones are wind-and-solar-powered unmanned surface vehicles rigged with atmospheric and oceanic sensors that measure upper ocean horizontal velocities, near-surface temperature and salinity, Chlorophyll-a fluorescence, dissolved oxygen concentration, 5-m winds, air temperature, and surface radiation. Acoustic Doppler Current Profiler (ADCP) data samples originally measured at 1 Hz frequency are averaged into 5 minute bins, along with navigation data. Non-ADCP data from IOP1 contain additional bio-optical measurements. All data are available in netCDF format.  ",
         "license": "proprietary"
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         "description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while  collecting profiles of oceanic variables. Data are available in netCDF format.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2830022538-POCLOUD.html",
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         "description": "This dataset contains shipboard Acoustic Doppler Current Profiler (ADCP) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign and two intensive operating periods (IOPs) conducted approximately 300 km offshore of San Francisco during Fall 2021, 2022, and Spring 2023.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The ADCP was mounted to the bottom of the hulls of the research vessels deployed during each campaign, measuring horizontal and vertical currents, as well as acoustic backscatter from approximately 3 m to 50 m depth along the ship\u2019s track. The data are available in netCDF format with dimensions of time and depth. ",
         "license": "proprietary"
     },
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         "description": "This dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data are available in netCDF format. ",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2830060262-POCLOUD.html",
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         "description": "This dataset contains in-situ seawater samples taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Water samples collected in Niskin bottles mounted on the ship\u2019s rosette sampler were taken of chlorophyll (\u00b5g/L), phaeopigments (\u00b5g/L), and nutrient concentrations (\u00b5M or \u00b5mol/L) of particulate organic carbon, particulate organic nitrogen, silicate, nitrate, nitrite, and phosphate. Samples analyzed with fluorometry contain chlorophyll concentrations in \u00b5g/L and phaeopigment concentrations in \u00b5g/L. Samples analyzed with elemental analysis contain POC molarity in \u00b5M and PON molarity in \u00b5M. Samples analyzed via ion analysis contain silicate concentrations in \u00b5M, total nitrate+nitrite in \u00b5M, phosphate in \u00b5M, nitrite in \u00b5M, and nitrate in \u00b5M. These data are mainly used by S-MODE for validating the PRISM-derived products and calibrating the in-situ sensors on the autonomous platforms. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "This dataset contains shipboard conductivity, temperature, and depth (CTD) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The shipboard CTD rosette is cast from the R/V Oceanus where it records ocean temperature, conductivity, and pressure as it descends to depth and then returns to the surface. IOP1 and IOP2 measurements also contain biological data. Data are available in netCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains shipboard radiometer measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Air-Sea Interaction METeorology (ASIMET) sensors mounted onboard the R/V Oceanus record shortwave and longwave radiation fluxes. These are used by S-MODE to compare with DopplerScatt retrievals. Data are available in netCDF format. ",
         "license": "proprietary"
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         "description": "This dataset contains atmospheric sounding measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023.  S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Sounding profiles were collected using shipboard Windsond S1H3-S radiosondes launched from the R/V Oceanus cruise OC2108A, to a maximum elevation of at least 5 km above ground level (ABL). These measurements are used to understand the vertical structure of atmospheric temperature, winds, and moisture. The original 1Hz observations were gridded onto a uniform 20 m vertical grid. The data are available in netCDF format with dimensions of altitude and profile number. ",
         "license": "proprietary"
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         "description": "This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.",
         "license": "proprietary"
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         "description": "This dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The TSG instrument measures the temperature and conductivity of seawater passing through a port in the hull of the ship. TSG data is calibrated using water samples compared to standard seawater and a laboratory salinometer onboard the ship. This dataset also contains chlorophyll and meteorology measurements including air temperature, barometric pressure, wind speed and direction, relative humidity, and radiative fluxes. Data are available in netCDF format, with separate dimensions for time, time of bio-optics measurements, and time of radiometer measurements.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2110184931-POCLOUD.html",
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         "description": "This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. The level 2 data herein combines measurements from both the UCDT and EcoCDT into a single dataset, where for each variable, all profiles are binned onto a 5m vertical grid and merged into a 2-D matrix. Additional computed variables include backscatter baseline signal and backscatter spike signal. Data are available in netCDF format.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2301083264-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2301083264-POCLOUD.html",
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         "description": "This dataset contains Slocum glider in-situ measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. US Naval Oceanographic Office (NAVOCEANO) Slocum gliders measure subsurface properties including temperature and salinity by profiling to a depth of 1000m at a fixed location every 4 hours. Data are available in netCDF format.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2574025518-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2574025518-POCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wicy1tb2RlIG1vc2VzIGxldmVsIDIgYXRtb3NwaGVyaWNhbGx5LWNvcnJlY3RlZCBzZWEgc3VyZmFjZSB0ZW1wZXJhdHVyZSB2ZXJzaW9uIDFcIixcIlBPQ0xPVURcIixcIlNNT0RFX0wyX01PU0VTX0xXSVJfU1NUX1YxXCIsXCIxXCIsMjExMDE4NDkyMSwyMF0iLCJ1bW0iOiJbXCJzLW1vZGUgbW9zZXMgbGV2ZWwgMiBhdG1vc3BoZXJpY2FsbHktY29ycmVjdGVkIHNlYSBzdXJmYWNlIHRlbXBlcmF0dXJlIHZlcnNpb24gMVwiLFwiUE9DTE9VRFwiLFwiU01PREVfTDJfTU9TRVNfTFdJUl9TU1RfVjFcIixcIjFcIiwyMTEwMTg0OTIxLDIwXSJ9/SMODE_L2_WAVEGLIDERS_TEMP_SALINITY_V1_1",
         "description": "This dataset contains waveglider observations from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Three wave gliders were deployed as part of the S-MODE pilot campaign, equipped with a suite of sensors including sonic anemometers, shortwave and longwave radiometers, CTD profilers, and ADCPs. All wave gliders include an IMU that records platform orientation and motion at 20Hz. Data are available in netCDF format.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2727951862-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2727951862-POCLOUD.html",
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         "description": "This dataset contains orthocorrected and atmospherically corrected water-leaving reflectance data from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco during Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Portable Remote Imaging Spectrometer (PRISM) is an airborne instrument package that is mounted on the GIII aircraft which flies long duration detailed surveys of the field domain during deployments. PRISM contains a pushbroom imaging spectrometer operating at near-UV to near-IR wavelengths (350-1050 nm), which produced high temporal resolution and resolve spatial features as small as 30 cm. PRISM also has a two-channel spot radiometer at short-wave infrared (SWIR) band (1240 nm and 1640 nm), that is co-aligned with the spectrometer and is used to provide accurate atmospheric correction of the ocean color measurements. Level 2a reflectance data is available in ENVI format. ",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2847092563-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2847092563-POCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wicy1tb2RlIGRvcHBsZXJzY2F0dCBsZXZlbCAxIHN1cmZhY2UgZG9wcGxlciBhbmQgcmFkYXIgYmFja3NjYXR0ZXIgdmVyc2lvbiAxXCIsXCJQT0NMT1VEXCIsXCJTTU9ERV9MMV9ET1BQTEVSU0NBVFRfVjFcIixcIjFcIiwyMzAxMDc2MTA3LDE2XSIsInVtbSI6IltcInMtbW9kZSBkb3BwbGVyc2NhdHQgbGV2ZWwgMSBzdXJmYWNlIGRvcHBsZXIgYW5kIHJhZGFyIGJhY2tzY2F0dGVyIHZlcnNpb24gMVwiLFwiUE9DTE9VRFwiLFwiU01PREVfTDFfRE9QUExFUlNDQVRUX1YxXCIsXCIxXCIsMjMwMTA3NjEwNywxNl0ifQ%3D%3D/SMODE_L3_SEAGLIDERS_TEMP_SALINITY_V1_1",
         "description": "This dataset contains profiles of temperature, dissolved oxygen, salinity, and other observations collected by Seagliders during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Seagliders are autonomous underwater vehicles (AUVs) designed to glide from the ocean surface to as deep as 1000 m and back while  collecting profiles of oceanic variables. Data are available in netCDF format.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2574152934-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2574152934-POCLOUD.html",
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         "description": "This dataset contains shipboard Underway conductivity, temperature, and depth (UCDT) measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Underway CTD system contains a standard UCDT probe measuring conductivity, temperature, and pressure, as well as an augmented EcoCDT probe that concurrently measures both hydrographic and bio-optical data including conductivity, temperature, pressure, dissolved oxygen concentration, chlorophyll-fluorescence, and particulate backscatter at two different wavelengths. Level 3 data are available in netCDF format with dimensions of profile number and depth.",
         "license": "proprietary"
     },
@@ -162573,7 +160402,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2988721782-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2988721782-POCLOUD.html",
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         "description": "This dataset contains model output from the Navy Coastal Ocean Model (NCOM) run during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. NCOM model output consists of daily files during the deployment dates of the pilot campaign in Fall 2021, IOP1 in Fall 2022, and IOP2 in Spring 2023. Data consists of ocean variables such as salinity, sea water temperature, water depth, and surface wind stress, and are available in netCDF format. ",
         "license": "proprietary"
     },
@@ -163717,7 +161546,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1550139370-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1550139370-NSIDC_ECS.html",
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         "description": "This data set contains brightness temperature readings. Brightness temperatures were measured using a KT-15 pyrometer flown on a P-3 aircraft over 2017 SnowEx campaign study areas in Colorado, USA: Grand Mesa, a snow-covered, forested study site about 40 miles east of Grand Junction; and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa. Data represents the nadir brightness temperature below the aircraft.",
         "license": "proprietary"
     },
@@ -163730,7 +161559,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1549510022-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1549510022-NSIDC_ECS.html",
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         "description": "This data set contains video footage of two locations in Colorado, USA: Grand Mesa, a snow-covered forested study site about 40 miles east of Grand Junction, and Senator Beck Basin, approximately 80 miles to the SSE of Grand Mesa. Video footage was captured using a video camera mounted to the belly of a P-3 aircraft during the 2017 SnowEx science flights. The video footage is raw.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1556467147-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1556467147-NSIDC_ECS.html",
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         "description": "This data set contains infrared camera images collected during the 2017 SnowEx campaign in Grand Mesa and Senator Beck Basin, Colorado. Images were taken using a Quantum Well Infrared Photodetector (QWIP) camera system manufactured by QmagiQ and mounted to the P-3 aircraft, which was flown over the study areas. The QWIP camera records images at a rate of 60 Hz and has a field of view of 11x9 degrees. The QWIP camera can distinguish temperature variations of approximately 0.02\u00b0C.  This data set contains raw QWIP data, presented as \"counts.\" Data are contained in TIFF image files with dimensions of 320x256 counts.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1597736755-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1597736755-NSIDC_ECS.html",
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         "description": "This data set consists of surface-based radiometer (SBR) brightness temperatures of the snow surface and vegetation at Grand Mesa, CO, USA, acquired during NASA's 2017 SnowEx campaign. Four SBRs were deployed in the field and obtained measurements at 89, 37, 19, and 10.67 GHz in both vertical (V-pol) and horizontal (H-pol) polarizations. Data are available for 37 locations across Grand Mesa collected over five days in February, 2017. They include both 2-4 minute observations and 20 minute, continuous measurements during surface melt, with the latter designed to capture the brightness temperature increase caused by the presence of liquid water in the snow.   This data set also contains snowfork measurements of snow wetness for 13 sites.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1720458466-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1720458466-NSIDC_ECS.html",
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         "description": "This data set consists of raw penetration force profiles measured at 8 different snow pits located in Senator Beck, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1423026967-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1423026967-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX17_SMP_1",
         "description": "This data set contains raw penetration force profiles measured at snow pits and along linear transects at Grand Mesa, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths.",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1460448213-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1460448213-NSIDC_ECS.html",
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         "description": "This data set reports vertical profiles of snow reflectance, specific surface area (SSA), and optical equivalent diameter (grain size) at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. Reflectance was measured <em>in situ</em> using a 1310\u00a0nm integrating sphere laser device and converted to SSA and optical equivalent diameter.",
         "license": "proprietary"
     },
@@ -163834,7 +161663,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1527470986-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1527470986-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX17_SSD_1",
         "description": "This data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east. The sensors were positioned to represent three primary vegetation conditions: open-canopy; canopy-edge; and closed-canopy. A total of 10 and 7 sensors recorded usable data at the west and east sites, respectively, from the beginning of the snow season in November 2016 through the end in June 2017.  These data can be used for a variety of purposes, including: model forcing, calibration, and validation; evaluation of airborne and satellite remote sensing data; to analyze how vegetation affects snow accumulation and ablation.",
         "license": "proprietary"
     },
@@ -163860,7 +161689,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1708383658-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1708383658-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX17_SnowSAR_1",
         "description": "This data set, part of the SnowEx 2017 campaign, contains multi-look synthetic aperture radar (SAR) amplitude images. The images are stored in netCDF files; browse images are also available. Data were collected using the SnowSAR instrument at the X (9.6 GHz) and Ku (17.25 GHz) bands. The SnowSAR instrument flew on the NP-3C Orion aircraft and captured data from across Colorado, including near the SnowEx 2017 Grand Mesa, Colorado study site and Vail, Colorado. Data were acquired between 16 February 2017 and 22 February 2017 and have a resolution of 1 meter by 1 meter.",
         "license": "proprietary"
     },
@@ -163873,7 +161702,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1722859882-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1722859882-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX17_SnowSAR_Raw_1",
         "description": "This data set, part of the SnowEx 2017 campaign, contains raw data captured from the SnowSAR instrument. The processed SnowSAR data are also archived at NSIDC (DOI: 10.5067/TWRTXCYBCBB8).  The SnowSAR instrument flew on the NP-3C Orion aircraft and collected data at the X (9.6 GHz) and Ku (17.25 GHz) bands. Data were captured across Colorado, including near the SnowEx 2017 Grand Mesa, Colorado study site and Vail, Colorado. Data were acquired between 16 February 2017 and 22 February 2017.",
         "license": "proprietary"
     },
@@ -163886,7 +161715,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2176470394-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2176470394-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX17_TLI_1",
         "description": "This data set contains time-lapse images. Cameras were placed around Grand Mesa, CO at 34 sites and around Senator Beck Basin, CO at one site, coincident with other SnowEx 2017 measurements, including the TLS scans, sonic snow depth arrays, weather stations, and local scale observation sites.",
         "license": "proprietary"
     },
@@ -163938,7 +161767,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1525987661-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1525987661-NSIDC_ECS.html",
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         "description": "This data set consists of ground-based scatterometer data acquired during the SnowEx 2017 campaign at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. The data comprise operational parameters used during data acquisition, Mueller matrices for each acquisition, and the nearfield-corrected normalized radar cross section (NRCS) in VV, VH, HV, and HH polarizations. Range profile data are also provided for each scan which report the raw power returned as a function of range from the antenna.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2053928182-NSIDC_ECS.html",
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         "description": "Gravimetric soil moisture data measured from collected soil samples in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. A total of 77 soil samples were taken at snow pit locations, soil moisture stations, and other areas where both the mobile and stationary COsmic-ray Soil Moisture Observing Systems (COSMOS) operated.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2107120672-NSIDC_ECS.html",
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         "description": "Snow depth was measured in autumn 2019 as part of the NASA SnowEx 2020 campaign at Grand Mesa, CO. Measurements were taken in the vicinity of snow pit locations and between pit locations using a Magnaprobe or manual depth probes.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2135287770-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2135287770-NSIDC_ECS.html",
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         "description": "This data set contains snow pit measurements obtained as part of the SnowEx 2020 campaign at the Grand Mesa, Colorado, USA site in Autumn 2019. 21 locations were visited for snow pit observations. Some snow pit measurements are incomplete due to shallow or discontinuous snow cover.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2053928728-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2053928728-NSIDC_ECS.html",
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         "description": "Snow water equivalent (SWE) measurements of the autumn shallow snow were obtained using SWE samplers (also referred to as SWE tubes) during the NASA SnowEx 2020 campaign (03-06 Nov. 2019) at Grand Mesa, CO. 221 measurements were taken in the vicinity of snow pit locations.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2759079563-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2759079563-NSIDC_ECS.html",
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         "description": "This data set contains raw penetration force profiles from the SnowEx 2020 Time Series campaign in the Boise River Basin in Idaho. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer, from three snow pits. The data files contain force measurements of the snowpack from the top of the snow surface to the ground. Measurements took place approximately weekly between 18 December 2019 and 24 January 2020.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1997892408-NSIDC_ECS.html",
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         "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the geolocated, unprocessed, common midpoint (CMP) gathers from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Multi-offset gathers were collected by placing antennas on the snow surface and expanding the antenna separation about a fixed midpoint out to a 2 m offset. CMP gathers were collected in HH and HV polarizations. Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8  were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3).  These data can be used to estimate snow depth, snow density, and snow water equivalent (SWE).",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1998359549-NSIDC_ECS.html",
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         "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain snow water equivalent (SWE) estimates. SWE is derived from Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR) two-way travel times.  Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8  were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3).  The raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.",
         "license": "proprietary"
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         "description": "This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the radar two-way travel times from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8  were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3).  The raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.",
         "license": "proprietary"
     },
@@ -164055,7 +161884,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1950140505-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1950140505-NSIDC_ECS.html",
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         "description": "This data set contains snow water equivalent (SWE) and snow depth estimates derived from Ground Penetrating Radar (GPR) measurements. GPR measurements were collected during the SnowEx 2020 Grand Mesa Intensive Observation Period (IOP) between 28 January and 04 February 2020. Snow depth was estimated from GPR two-way travel times and average radar velocity; SWE was estimated from snow depth and snow density.  These data are derived from the SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR Raw, Version 1 data set (DOI: 10.5067/CJNEM8UDNXKA)",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1937994967-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1937994967-NSIDC_ECS.html",
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         "description": "This data set contains the geolocated, unprocessed results of a ground penetrating radar survey conducted as part of the 2020 SnowEx campaign. Data were collected at Grand Mesa, Colorado (a snow-covered, forested study site) between 28 January 2020 and 04 February 2020.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2025305209-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2025305209-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX20_COCP_GPR_1",
         "description": "This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 18 December 2019 and 12 March 2020",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2025305138-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2025305138-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIiwidW1tIjoiW1wic25vd2V4MTcgZ3JvdW5kIHBlbmV0cmF0aW5nIHJhZGFyIHYwMDJcIixcIk5TSURDX0VDU1wiLFwiU05FWDE3X0dQUlwiLFwiMlwiLDE2NTU4NzU3MzcsODFdIn0%3D/SNEX20_COCP_GPR_Raw_1",
         "description": "This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data were collected between 18 December 2019 and 12 March 2020.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2888869172-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2888869172-NSIDC_ECS.html",
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         "description": "This data set contains the results of ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx20 campaign. Data include two-way travel time, lidar-measured snow depth, derived snow water equivalent, derived snow density, and derived relative permittivity. Ground-penetrating radar two-way travel times were sourced from two previously published data sets: <a href=\"https://doi.org/10.5067/U4Q3X27BMRR4\">SnowEx20 Cameron Pass Ground Penetrating Radar, Version 1</a> and <a href=\"https://doi.org/10.5067/SRWGLYCB6ZC4\">SnowEx21 Cameron Pass Ground Penetrating Radar, Version 1</a>. The new data presented here represents a recalculation of the snow water equivalent (SWE), snow density, and relative permittivity using terrestrial lidar scan (TLS) data.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1997892324-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1997892324-NSIDC_ECS.html",
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         "description": "This data set contains the raw and processed data files from a COSMOS Rover soil moisture probe. The COSMOS Rover uses fast neutron counting to estimate soil moisture over a region up to 300m from the vehicle. Data were collected in one-minute intervals over four days (04 November to 07 November 2019) of driving around Grand Mesa, Colorado. Raw data parameters include atmospheric pressure, temperature, and relative humidity. These raw observations were converted to volumetric soil moisture in the processed data files.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1997891597-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1997891597-NSIDC_ECS.html",
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         "description": "These data contain raw and processed hourly observations from a Hydroinnova COSMOS Stationary sensor probe. Parameters in the raw files include atmospheric pressure, temperature, and relative humidity. These observations were converted to volumetric soil moisture in the processed data files. Data were collected between 26 August 2019 and 31 May 2020 at Grand Mesa, Colorado and represent a 200 m to 300 m area around the instrument.",
         "license": "proprietary"
     },
@@ -164146,7 +161975,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2053929094-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2053929094-NSIDC_ECS.html",
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         "description": "This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2020 field campaign. Soil moisture probes were deployed at 10 stations within the Colorado Grand Mesa study area and monitored soil properties at three different depths (5, 10, and 20 cm).",
         "license": "proprietary"
     },
@@ -164159,7 +161988,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2107120625-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2107120625-NSIDC_ECS.html",
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         "description": "This data set contains geolocation information of the infrastructure locations for the SnowEx20 Intensive Observation Period (IOP) and Time Series (TS) campaigns. Available scientific infrastructure locations in this data set are tower and sensor locations, aircraft flight lines, planned and actual snow pit locations, and time-lapse camera locations. Additionally, this data set contains areal snow depth and tree density classification matrix over the Grand Mesa, CO study area.",
         "license": "proprietary"
     },
@@ -164172,7 +162001,7 @@
         "bbox": "-108.25, 39.015, -108, 39.04",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2083503776-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2083503776-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjAgY29zbW9zIHJvdmVyIHNvaWwgbW9pc3R1cmUgdjAwMVwiLFwiTlNJRENfRUNTXCIsXCJTTkVYMjBfQ1JTTVwiLFwiMVwiLDE5OTc4OTIzMjQsODZdIiwidW1tIjoiW1wic25vd2V4MjAgY29zbW9zIHJvdmVyIHNvaWwgbW9pc3R1cmUgdjAwMVwiLFwiTlNJRENfRUNTXCIsXCJTTkVYMjBfQ1JTTVwiLFwiMVwiLDE5OTc4OTIzMjQsODZdIn0%3D/SNEX20_GM_CSU_GPR_1",
         "description": "This data set contains two-way travel times, snow depth and snow water equivalent (SWE) collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020.  These data are derived from the SnowEx20 Grand Mesa IOP CSU 1GHz GPR Raw, Version 1 data set (DOI: 10.5067/CT6NS2LIASRS)",
         "license": "proprietary"
     },
@@ -164185,7 +162014,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2083503967-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2083503967-NSIDC_ECS.html",
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         "description": "This data set the raw files collected with a Sensors & Software 1GHz ground penetrating radar (GPR) as part of the SnowEx 2020 Intensive Observation Period (IOP) at Grand Mesa, Colorado between 06 February 2020 and 09 February 2020.  The more processed data are published in SnowEx20 Grand Mesa IOP CSU 1GHz GPR, Version 1 (DOI: 10.5067/S5EGFLCIAB18)",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2695623073-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2695623073-NSIDC_ECS.html",
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         "description": "This data set characterizes snow microstructure for 6 snow pits from the SnowEx 2020 Grand Mesa Intensive Observation Period (February 2020) using microcomputed tomography (micro-CT). Included with this data set are two- and three-dimensional microstructural analysis of DMP/DEP casted and un-casted snow samples, available as .xlsx and .txt file, and visual representations of the three-dimensional snow structure, available is .bmp image files. Containers of snow were collected at 6 snow pits in approximately 17 cm intervals. There were approximately 5-8 discrete containers per pit and each container had an ~2 cm overlap with the sample below. A 6-cm section of snow was dissected from each container of snow, which were analyzed in ~2 cm sub-sections. Each sub-section was scanned in three intervals using a micro-CT instrument. The three interval scans comprise multiple slices, and were combined into the reconstructed final scan used for calculating the snow microstructural data.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3142513079-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3142513079-NSIDC_ECS.html",
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         "description": "This data set contains rasterized snow depth maps derived from lidar point cloud data collected from Grand Mesa, Colorado during the SnowEx20 campaign. The subset data file was used as input data to derive SWE and snow density data, which is available as <a href=\"https://doi.org/10.5067/LANQ53RTJ2DR\">SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent and Snow Density, Version 1</a>.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1990738958-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1990738958-NSIDC_ECS.html",
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         "description": "This data set contains snow pit observations from the SnowEx 2020 Grand Mesa Intensive Observation Period. Data were collected from 154 snow pits on Grand Mesa, Colorado between 27 January and 12 February 2020. The main parameters for this data set are snow temperature, snow depth, snow density, snow stratigraphy, snow grain size, liquid water content, and snow water equivalent. In addition to data files, this data set also contains site photos from each snow pit.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3140422168-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3140422168-NSIDC_ECS.html",
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         "description": "This data set contains snow water equivalent (SWE) and snow density raster data derived from a lidar-based snow depth data set (<a href=\"https://doi.org/10.5067/M9TPF6NWL53K\">SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1</a>), a lidar-based digital terrain model (<a href=\"https://doi.org/10.5067/2EHMWG4IT76O\">ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid, Version 1</a>), and two GPR data sets (<a href=\"https://doi.org/10.5067/Q2LFK0QSVGS2\">SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1</a> and <a href=\"https://doi.org/10.5067/WE9GI1GVMQF6\">Mesa IOP UNM 800 and 1600 MHz MALA GPR, Version 1</a>). Data was collected during the NASA SnowEx 2020 field campaign in Grand Mesa, Colorado.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2059268385-NSIDC_ECS.html",
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         "description": "This data set contains two-way travel times from a ground penetrating radar survey conducted at Jemez, New Mexico. Data were collected between 12 February 2020 and 04 March 2020 as part of the SnowEx 2020 campaign.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2435982480-NSIDC_ECS.html",
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         "description": "This data set is part of the SnowEx 2021 campaign and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 0.5 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/8RBIUIPEUJ7Z\">Vegetation Height</a> and <a href=\"https://doi.org/10.5067/VBUN16K365DG\">Snow Depth</a> data sets.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2603552400-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2603552400-NSIDC_ECS.html",
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         "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides bare Earth digital elevation models (DEM) acquired by a scanning lidar system at a 3.0 m spatial resolution, and derived from point cloud digital terrain models. DEMs are available for September 2021 and from multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/K67GKZQ9WUEI\">Vegetation Height</a> and <a href=\"https://doi.org/10.5067/4VO7438DP332\">Snow Depth</a> data sets.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2435980844-NSIDC_ECS.html",
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         "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/YO583L7ZOLOO\">DEM</a> and <a href=\"https://doi.org/10.5067/8RBIUIPEUJ7Z\">Vegetation Height</a> data sets.",
         "license": "proprietary"
     },
@@ -164302,7 +162131,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2603552692-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2603552692-NSIDC_ECS.html",
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         "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides snow depth values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Snow depths are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/VV382L4MQI7V\">DEM</a> and <a href=\"https://10.5067/K67GKZQ9WUEI\">Vegetation Height</a> data sets.",
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         "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 0.5 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/YO583L7ZOLOO\">DEM</a> and <a href=\"https://doi.org/10.5067/VBUN16K365DG\">Snow Depth</a> data sets.",
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         "description": "This data set is part of the SnowEx 2020 and SnowEx 2021 campaigns and provides vegetation height values at a 3.0 m spatial resolution, derived from point cloud digital terrain models. Vegetation heights are available for February 2020 and March 2021 for multiple areas in Colorado, Idaho, and Utah. These data were produced alongside <a href=\"https://doi.org/10.5067/VV382L4MQI7V\">DEM</a> and <a href=\"https://doi.org/10.5067/4VO7438DP332\">Snow Depth</a> data sets.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1943097217-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1943097217-NSIDC_ECS.html",
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         "description": "This data set contains hourly ground surface temperature measurements collected between 20 October 2019 and 18 July 2020. Data were collected at 7 points along two plant monitoring transects in the Upper Basin of the Senator Beck Study Basin. Temperatures were measured using iButton temperature sensors.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1917766830-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1917766830-NSIDC_ECS.html",
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         "description": "This data set, collected during the SnowEx 2020 Intensive Operation Period (IOP) in Grand Mesa, Colorado, contains in situ snow depth measurements. Snow depth was measured using one of three instruments - Magnaprobe, Mesa 2, or pit ruler. Pit ruler data were collected from 150 snow pits identified for the Grand Mesa IOP. Magnaprobe and Mesa 2 data were collected along spiral tracks moving outwards from snow pit locations. This data set has a temporal coverage from 28 January to 12 February 2020.",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2236390694-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2236390694-NSIDC_ECS.html",
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         "description": "This data contains snow depth measurements derived from time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. The time-lapse images are archived separately at NSIDC (SNEX20_TLI).",
         "license": "proprietary"
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931426050-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931426050-NSIDC_ECS.html",
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         "description": "This data set contains raw penetration force profiles from the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Measurements were taken using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths. Data are available from 28 January 2020 through 12 February 2020.",
         "license": "proprietary"
     },
@@ -164393,7 +162222,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1970755314-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1970755314-NSIDC_ECS.html",
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         "description": "This data set contains vertical profiles of snow reflectance, specific surface area (SSA), and spherical equivalent snow grain diameter. Data were collected during the SnowEx 2020 Grand Mesa, Colorado Intensive Observation Period between 27 January and 12 February 2020. Reflectance was measured at snow pits using one of two integrating sphere laser devices, IRIS (InfraRed IntegraXng Sphere) or IceCube. SSA and spherical equivalent diameter were then derived from reflectance.",
         "license": "proprietary"
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@@ -164406,7 +162235,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2068420609-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2068420609-NSIDC_ECS.html",
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         "description": "This data set contains airborne microwave brightness temperature observations from the Goddard Space Flight Center SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument during the winter (10-12 February 2020) NASA SnowEx 2020 campaign at Grand Mesa, CO. Observations were made at three frequencies (10.65, 18.7, and 36.5 GHz; referred to as X, K, and Ka bands, respectively), at horizontal polarization with a nominal 45-degree look angle.",
         "license": "proprietary"
     },
@@ -164419,7 +162248,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2214722684-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2214722684-NSIDC_ECS.html",
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         "description": "This data set contains sub-daily time-lapse images collected by cameras placed around Grand Mesa, CO at 29 sites coincident with other SnowEx 2020 measurements. The field view of all cameras includes a 3.049 m, (10 ft) vertical pole that was painted red with a yellow top to serve as a reference for quantifying snow depth. Snow depth data derived from these time-lapse images will be published separately at NSIDC.",
         "license": "proprietary"
     },
@@ -164432,7 +162261,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1996524638-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1996524638-NSIDC_ECS.html",
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         "description": "This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.",
         "license": "proprietary"
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         "description": "This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). A processed version of this data set can be found here: https://doi.org/10.5067/F0M99WK7JW4X.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1937994549-NSIDC_ECS.html",
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         "description": "This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2020 SnowEx campaign. Data were collected in fall 2019 (September) and winter 2020 (January and February). Each data file contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2929469669-NSIDC_ECS.html",
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         "description": "The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations.  Also available are photos of the field notes and snow pit sites.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2059268334-NSIDC_ECS.html",
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         "description": "This data set contains two-way travel times, snow depth and now water equivalent from a ground penetrating radar survey conducted at Grand Mesa, Colorado. Data were collected between 28 January 2020 and 06 February 2020 as part of the SnowEx 2020 campaign.",
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1997892310-NSIDC_ECS.html",
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         "description": "This data set contains an eight-day time series of vertical temperature profile measurements. Measurements were collected using a thermoprobe to a depth of 30 cm at two site locations. These data were collected as part of the SnowEx 2020 Intensive Observation Period in Grand Mesa, Colorado. Alongside temperature profiles, this data set contains snow depth measurements, manual temperature measurements, and thermal infrared camera images. These data are published without QA/QC or calibration with manual measurements.",
         "license": "proprietary"
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         "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data include two-way travel time, pit-measured snow density, calculated snow depth, and calculated snow water equivalent. Data were collected between 13 January 2021 and 27 May 2021.",
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         "description": "This data set contains the raw files from ground-penetrating radar surveys conducted at Cameron Pass, Colorado during the SnowEx21 campaign. Data were collected between 13 January 2021 and 27 May 2021.",
         "license": "proprietary"
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         "description": "This data set consists of soil moisture and soil temperature measurements taken during the SnowEx 2021 field campaign. Soil moisture probes were deployed at 9 locations within the Montana Prairie Station study area and monitored soil properties at four different depths (5, 10, 20 and 50 cm).",
         "license": "proprietary"
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@@ -164549,7 +162378,7 @@
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         "description": "This dataset consists of Digital Surface Models (DSMs) derived from UAV-LiDAR acquired during the SnowEx 2021 field campaign at the Central Agricultural Research Center in central Montana (USA). The DSMs are at 0.3 m resolution and consist of one snow-off and seven snow-on flights.",
         "license": "proprietary"
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@@ -164562,7 +162391,7 @@
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         "description": "This data set contains observations from a meteorological station installed at the Central Agricultural Research Center in Moccasin Montana as part of the NASA SnowEx 2021 Prairie Snow field campaign. Parameters include: air temperature, wind speed and direction, and barometric pressure. Data are available from 12 November 2020 through 2 March 2021.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2560987849-NSIDC_ECS.html",
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         "description": "This data set provides apparent surface spectral reflectance imagery which demonstrates snow albedo and snow optical property evolution across two distinct snow-covered environments in Colorado. Data collection occurred in the spring of 2021 as part of the NASA SnowEx mission. The two study sites (Senator Beck Basin and Grand Mesa) were chosen for their contrasting terrain and vegetation characteristics. Data collection occurred over three days (19 March, 1 April, and 29 April) to produce a time series data set across varying snow conditions.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3046987606-NSIDC_ECS.html",
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         "description": "The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. Between November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States. Five of the unique sites had a single visit to establish baseline conditions, while the remaining 19 sites had 3 or more repeat visits throughout the season, with a median visit count of 11.5. On a weekly interval, a snow pit was dug approximately 1 m away from the previous week\u2019s snow pit. Available measured parameters are: snow depth, snow temperature, snow density, stratigraphy, grain size, manual wetness, liquid water content (LWC), and snow water equivalent (SWE). Also available are photos of the field notes and snow pit sites.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2854618795-NSIDC_ECS.html",
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         "description": "This data set contains digital terrain models (DTMs) derived from terrestrial lidar scans (TLS) collected as part of the SnowEx 2023 campaign. Data were collected at the Bonanza Creek Experimental Forest near Fairbanks, Alaska in October 2022 (snow-off conditions) and March 2023 (snow-on conditions). The DTMs are provided as Geographic Tagged Image (GeoTIFF) files, where each file corresponds to a unique survey site. Unprocessed point cloud data from which these DTMs were derived are available as the <a href=\"https://doi.org/10.5067/OIBBFR49P0Q6\">SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans Raw, Version 1 (SNEX23_BCEF_TLS_Raw) data set</a>",
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         "description": "This data set contains unprocessed point cloud data created from terrestrial lidar scans (TLS) collected during the SnowEx 2023 campaign from the Bonanza Creek Experimental Forest near Fairbanks, Alaska. Data were collected in October 2022 (snow-off) and March 2023 (snow-on). Digital terrain models (DTMs) derived from the raw point cloud data are available as the <a href=\"https://doi.org/10.5067/R466GRXNA61S\">SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans, Version 1 (SNEX23_BCEF_TLS) data set</a>",
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         "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include two-way travel (TWT) time, calculated snow depth, and calculated snow water equivalent (SWE). Raw GPR data are available as <a href=\"https://nsidc.org/data/SNEX23_CRREL_GPR_Raw/versions/1\">SnowEx23 CRREL Ground Penetrating Radar Raw, Version 1</a>.",
         "license": "proprietary"
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         "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Upper Kuparuk/Toolik (UKT) site in northern Alaska, USA as part of the NASA SnowEx 2023 field campaign. Data were collected between 08 Mar 2023 to 15 Mar 2023, spatially coinciding with snow pit locations and along transects between snow pits. Data include georeferenced multichannel ground-penetrating radargrams stored within .nc files. PRocessed GPR data are available as <a href=\"https://nsidc.org/data/SNEX23_CRREL_GPR/versions/1\">SnowEx23 CRREL Ground Penetrating Radar, Version 1</a>.",
         "license": "proprietary"
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         "description": "This data set contains the raw results of 1 GHz ground-penetrating radar surveys conducted as part of the NASA SnowEx23 field campaign in Alaska, USA. Surveys were conducted at three different field sites between 07 March 2023 and 16 March 2023: 1) Farmers Loop/Creamers Field, 2) the Bonanza Creek Experimental Forest, and 3) the Caribou/Poker Creek Research Watershed.",
         "license": "proprietary"
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@@ -164666,7 +162495,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2893208516-NSIDC_ECS.html",
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         "description": "This data set provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). The raw data from which these data are derived are available as <a href=\"https://nsidc.org/data/SNEX23_Lidar_Raw\">SnowEx23 Airborne Lidar Scans Raw, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164679,7 +162508,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2893835306-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_Lidar_Raw_1",
         "description": "This data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). Processed data, including digital terrain models, snow depth, and canopy height derived from Point Cloud Digital Terrain Models (PCDTMs) are available as <a href=\"https://nsidc.org/data/SNEX23_Lidar\">SnowEx23 Airborne Lidar-Derived 0.25M Snow Depth and Canopy Height, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164692,7 +162521,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3154261714-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3154261714-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_MAR22_SD_1",
         "description": "The data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain). Snow depth measurements collected from the study sampling sites during the subsequent field season are available as <a href=\"https://nsidc.org/data/snex23_mar23_sd/versions/1\">SnowEx23 Mar23 IOP Snow Depth Measurements, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164705,7 +162534,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3172387010-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3172387010-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_MAR23_SD_1",
         "description": "The data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer\u2019s Loop/Creamer\u2019s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik). Snow depth measurements collected from the study sampling sites during the previous field season are available as <a href=\"https://nsidc.org/data/snex23_mar22_sd/versions/1\">SnowEx23 Mar22 IOP Snow Depth Measurements, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164718,7 +162547,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2735033831-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2735033831-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_SSA_1",
         "description": "This data set contains vertical profiles of snow reflectance and specific surface area (SSA) from the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer\u2019s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik), collected as part of the NASA SnowEx 2023 field campaign in March 2023. Reflectance was measured in snow pits using three different integrating sphere laser devices: an A2 Photonic Sensor IceCube (1310 nm), an IRIS (InfraRed Integrating Sphere) system (1310 nm), and an InfraSnow SSA sensor (945 nm). Measured reflectance values were converted to SSA during data processing. It is recommended that data users work with either the IceCube or IRIS data, as the InfraSnow data was collected primarily for testing of the instrument\u2019s capabilities. Snow-off SSA data from these same study sites are available as <a href=\"https://nsidc.org/data/snex23_ssa_so/versions/1\">SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164731,7 +162560,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2881748646-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2881748646-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_SSA_SO_1",
         "description": "This data set reports vertical profiles of snow reflectance and specific surface area (SSA) from two study sites in Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer\u2019s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik). Reflectance was measured in situ using an A2 Photonic Sensor IceCube (1310 nm). Measured reflectance values were converted to SSA during data processing following the methods of Gallet et al., (2009). Snow-on SSA data from these same study sites were collected in March 2023 and are available as <a href=\"https://nsidc.org/data/snex23_ssa/versions/1\">SnowEx23 Laser Snow Microstructure Specific Surface Area Data, Version 1</a>.",
         "license": "proprietary"
     },
@@ -164744,7 +162573,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3041011983-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3041011983-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_SWE_1",
         "description": "This data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.",
         "license": "proprietary"
     },
@@ -164757,7 +162586,7 @@
         "bbox": "-149.54981, 68.58784, -148.61304, 70.0846",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2917647163-NSIDC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2917647163-NSIDC_ECS.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIiwidW1tIjoiW1wic25vd2V4MjEgcHJhaXJpZSBzdGF0aW9uIGluIHNpdHUgZGllbGVjdHJpYyBzb2lsIG1vaXN0dXJlIGFuZCBzb2lsIHRlbXBlcmF0dXJlIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU05FWDIxX0RTTVwiLFwiMVwiLDIyMTU1NzQxODQsNzBdIn0%3D/SNEX23_UW_GPR_1",
         "description": "This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Arctic Coastal Plain (ACP) site and the Upper Kuparuk/Toolik (UKT) site in northern Alaska during the SnowEx23 field campaign. Data include two-way travel time, derived snow depth, and derived snow water equivalent. Data were collected between 8 - 14 March 2023.",
         "license": "proprietary"
     },
@@ -164774,6 +162603,32 @@
         "description": "This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa, Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pl\u00e9iades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.",
         "license": "proprietary"
     },
+    {
+        "id": "SNEX_MCS_Lidar_1",
+        "title": "SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey V001",
+        "catalog": "NSIDC_ECS STAC Catalog",
+        "state_date": "2021-03-10",
+        "end_date": "2024-04-18",
+        "bbox": "-115.7304987, 43.9136453, -115.6424377, 43.9748021",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_1",
+        "description": "The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as  <a href=\"https://nsidc.org/data/SNEX_MCS_Lidar_Raw\">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1</a>) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.",
+        "license": "proprietary"
+    },
+    {
+        "id": "SNEX_MCS_Lidar_Raw_1",
+        "title": "SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw V001",
+        "catalog": "NSIDC_ECS STAC Catalog",
+        "state_date": "2021-03-10",
+        "end_date": "2024-04-18",
+        "bbox": "-115.7304987, 43.9136453, -115.6424377, 43.9748021",
+        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.umm_json",
+        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.html",
+        "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections?cursor=eyJqc29uIjoiW1wic21hcHZleDE5LTIyIG1hc3NhY2h1c2V0dHMgdGVtcG9yYXJ5IHNvaWwgbW9pc3R1cmUgbmV0d29yayB2MDAxXCIsXCJOU0lEQ19FQ1NcIixcIlNWMTlNQV9UTkVUXCIsXCIxXCIsMjY1NDQ2OTk3Myw2OV0iLCJ1bW0iOiJbXCJzbWFwdmV4MTktMjIgbWFzc2FjaHVzZXR0cyB0ZW1wb3Jhcnkgc29pbCBtb2lzdHVyZSBuZXR3b3JrIHYwMDFcIixcIk5TSURDX0VDU1wiLFwiU1YxOU1BX1RORVRcIixcIjFcIiwyNjU0NDY5OTczLDY5XSJ9/SNEX_MCS_Lidar_Raw_1",
+        "description": "The data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km\u00b2 region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.   Digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as <a href=\"https://nsidc.org/data/SNEX_MCS_Lidar\">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1</a>.",
+        "license": "proprietary"
+    },
     {
         "id": "SNEX_Met_1",
         "title": "SnowEx Meteorological Station Measurements from Grand Mesa, CO V001",
@@ -167578,7 +165433,7 @@
         "bbox": "-73, 20, -28, 42",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772166-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2491772166-POCLOUD.html",
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.   Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations.  Shipborne ADCP data were collected during the 3 US cruises, using the Knorr and Endeavor 300 kHz Workhorse, 75 khz broadband and 75 khz narrowband instruments, and during the Sarmiento cruise using a 76.8 khz broadband ADCP.  Corresponding ruise dates were as follows: Knorr: 6 Sept-9 Oct 2012; Sarmiento: 14 Mar-10 Apr 2013, Endeavor: 15 Mar-15 Apr 2013 and 19 Sep-13 Oct 2013.  Additionally, lowered ADCP (L-ADCP) measurements were made during the Knorr cruise on every CTD cast and during the Sarmiento cruise.   The ADCP data files here (1 per cruise) are for the shipborne ADCP measurements only.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. CTD (Conductivity, Temperature, Depth) profilers were deployed at stations on each of the 5 SPURS-1 cruises.  These shipboard lowered CTD probes provide continuous conductivity, salinity, and temperature vertical profile observations at fixed sampling locations. There were 100, 52, 17, 22 and 94 CTD casts made during the Knorr, Endeavor-1, Endeavor-2, Sarmiento, and Thalassa cruises respectively. All CTD data were calibrated using shipboard salinometers using IAPSO standard seawater.  SPURS-1 shipboard CTD data files (one per cruise) contain the observational data processed to 1 meter bin depth intervals.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  Approximately 83 drifters were deployed during the SPURS-1 campaign. A drifter is a passive Lagrangian sensor platform consisting of a surface buoy and tethered subsurface drogue. Drifter buoys contain GPS/ARGOS and satellite data transmitters, with sensors measuring temperature and other properties. For SPURS-1, these were standard Surface Velocity Program (SVP) drifters with salinity sensors added (SVP/S). Data for both US and European drifter deployments during SPURS-1 are available here.  For each series, drifter data have been aggregrated within single netCDF data filea with their corresponding drifter-IDs and associaciated near-surface salinity, temperaure georeferenced (GPS and ARGOS) trajectory series data.",
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Ecomapper or IVER is a portable autonomous underwater vehicle (AUV) capable carrying a range of sensor payloads.  For SPURS-1 these included CTD, chlorophyll, oxygen and turbidity sensors. Ecomapper was deployed on two days during the Knorr cruise, 29 and 30 September 2012.  The resulting Knorr Ecomapper data files include deployment event information and contain  trajectory-depth profile series  of chlorophyll, turbidity, oxygen, conductivity, with salinity, and temperature observations from two sensors.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  Neutrally buoyant floats drift and move through the water column providing continuous temperature and salinity profiles via 2 integrated CTDs and GPS surface position location data. Two floats were deployed during SPURS-1, one during the Knorr cruise in September 2012 another deployed during the April 2013 Endeavor cruise.  Recoveries were in April and September 2013 respectively.  Neutrally buoyant float trajectory profile data include georeferenced time series of salinity, temperature, and pressure/depth observations.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. All US SPURS-1 cruises (Knorr: 6 Sept-9 Oct 2012; Endeavor: 15 Mar-15 Apr 2013 and 19 Sep-13 Oct 2013) were equipped with a ship mast meteorological sensor package. An additional set of sophisticated meteorological sensors, including a direct covariance flux package, was installed on the Knorr. These sensors provided along-track atmospheric pressure, temperature, humidity, IR/visible radiation, rain, wind speed and direct covariance flux measurements.  Resulting data files (1 per cruise) contain these georeferenced, SPURS-1 research vessel-based meteorological measurements.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  Two PICO moorings (PICO-1000, PICO-3000) were deployed on the Knorr cruise in September 2012 in the northern and eastern SPURS-1 domain quadrants at N24.74, W37.95 and N24.51, W37.81 respectively. The moorings contained a surface meteorological package and a \"prawler\", a CTD that crawls up and down the mooring line from the near-surface down to about 500m, yielding time series of salinity and temperature profile data at fixed locations. The moorings were recovered on the Endeavor-2 cruise.  PICO mooring netCDF files contain georeferenced CTD profile data including salinity, temperature, potential temperature, pressure, depth, meteorological variables, GPS-Lat/Lon, and profile ID.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The SPURS central mooring consisted of a surface meteorological package, surface oceanographic instruments, and subsurface, non-real time oceanographic instruments including CTD, ADCP sensors and current meters providing continuous series of temperature, salinity and current profile observations. Meteorological observations include wind speed, air temperature, precipitation, and radiative flux. The mooring was deployed in 5,535 meters of water at N24:34.867, W38 on 14 September 2012, was serviced on 25 March 2013 and recovered on 30 September 2013.  WHOI mooring  data files include surface and subsurface time series  of sea temperature, skin temperature, salinity, conductivity, wind velocity, air temperature, relative humidity, precipitation rate, barometric pressure, shortwave and longwave radiation, short/longwave flux, heat Flux, wind Speed and direction.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  The Seaglider is an autonomous profiler measuring salinity and temperature. Three Seagliders were deployed on the Knorr cruise in September 2012. These were retrieved during the first Endeavor cruise, and then redeployed. The Seagliders typically made loops or butterfly patterns around the central SPURS mooring, diving to 1000 m.  Seaglider data files contain vertically resolved trajectory series  of conductivity, salinity, temperature, pressure, depth observations.",
         "license": "proprietary"
     },
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  The Seasoar is a towed vehicle equipped with impeller-forced wings that can be rotated on command to allow the vehicle to undulate in the upper ocean. Generally, Seasoar operates between the surface and about 400 meters depth while being towed on faired cable at about eight knots. A typical dive cycle takes about 12 minutes to complete, providing an up and down profile every 3 km.  For SPURS-1, a Seasoar was deployed exclusively during the Sarmiento cruise over the period 22 Mar-8 Apr, 2013 and to a maximum depth of 312m.  The Seasoar towed sensor system was equipped with dual pumped temperature/conductivity sensors.  The Seasoar data in netCDF form here contains a highly processed 1-meter gridded version of the original source dataset, which is comprised of temperature, conductivity, salinity, pressure observations from 1144 casts during 2013 Spring SPURS Cruise.",
         "license": "proprietary"
     },
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. The Tenuse (Slocum) glider is an autonomous undulating profiler measuring salinity and temperature.  It was deployed from the Thalassa on 21-August and recovered by the Knorr on 4-October-2012. It made a total of about 1400 profiles during that period (1-2 profiles/hour), going from the surface to 200 m. Resulting  trajectory profile data from the Tenuse glider include georeferenced CTD observations on salinity, temperature, pressure, and depth.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W.  All SPURS-1 vessels were equipped with a thermosalinograph (TSG). A TSG is an automated measurement system that is coupled to a research vessel's water intake and GPS systems to provide continuous, along-track surface temperature and salinity measurements. Each SPURS cruise employed TSGs whose measurements were calibrated  against onboard salinometers.  TSG data files are one per cruise.  Note that Knorr TSG data are contained coupled in the same file as its shipborne meteorological observations.",
         "license": "proprietary"
     },
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. An Underway-CTD (UCTD) was deployed on 2 of the SPURS-1 cruises.  An UCTD is a towed CTD instrument providing conductivity, salinity and temperature depth profile observations while underway at up to 20kts. 771 UCTD casts occurred during the Knorr and Endeavor-I cruises (6 Sept-9 Oct 2012 and 15 Mar-15 Apr 2013 respectively) utilizing an Oceanscience instrument.  UCTD data files (1 per cruise) each contain the observational data for multiple deployments, binned in 1m depth intervals.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is an oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes.  SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales.  The SPURS-1 campaign involved a series of 5 cruises during 2012 - 2013 seeking to characterize the salinity structure and balance in a high salinity, high evaporation, and low rainfall region of the subtropical North Atlantic. It aims to resolve processes responsible for maintaining the subtropical surface salinity maximum in this region and within a 900 x 800-mile square study area centered at 25N, 38W. A Waveglider is an autonomous platform propelled by the conversion of ocean wave energy into forward thrust and employing solar panels to power instrumentation. During SPURS-1, three wavegliders (ASL2, ASL3 and ASL4) were deployed from the Knorr in September 2012, redeployed in April 2013 (ASL22, ASL32 and ASL42) with final recovery in September. Waveglider trajectories followed a square loop or butterfly pattern around the central SPURS mooring.  Sensors included a CTD at the near-surface and another at 6 m depth, a surface current meter, air temperature, atmospheric pressure and wind speed sensors providing continuous  along-track observations.  NetCDF waveglider data files here contain hour averaged, georeferenced  trajectory data for those parameters and depths.",
         "license": "proprietary"
     },
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D,SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Shipborne ADCP observations were made during both SPURS-2 R/V Revelle cruises. Acoustic Doppler Current Profilers (ADCP) provide water column current velocity profile observations. The resulting data files available here are for narrowband 75 and 150khz ADCP measurements made during the first cruise, plus narrowband (NB) 75khz and both 75khz and 150khz broadband (BB) ADCP measurements obtained during the second R/V Revelle cruise.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth (CTD) profiles from the upper 2000m. Twenty five floats were deployed during SPURS-2 within the campaign spatial domain and time period, yielding approximately 1,893 profiles. These were standard Argo floats with the addition of acoustic rain gauges (PAL) in some cases. SPURS-2 ARGO data files are organized per float and profile with the vertical conductivity, salinity, temperature, pressure, depth observations per the netCDF ARGO file specification with some augmented global metadata attributes.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Controlled Flux Technique (CFT) is a system for measuring the net heat transfer velocity and turbulent kinetic energy (TKE) dissipation at the ocean surface, and is a useful tool for studying the turbulence generated at the ocean surface by the impact of raindrops. CFT was employed during both SPURS-2 Revelle cruises. It involves a laser heating a small patch of water on the ocean surface, and an infrared imaging camera then tracking the resulting thermal decay.  This decay is known to be proportional to the dissipation of TKE at the water surface, which in turn can be used to scale the transfer velocity for the net heat flux. SPURS2 CFT data take the form of a series of .raw video files each with corresponding .met text header files containing the associated file metadata.  The CFT data was recorded at 15 frames per second (fps) during the first Revelle cruise in 2016, and at 25 fps during the second in 2017.  Matlab CFT reader software are provided by UW/APL and distributed here with the CFT data files.",
         "license": "proprietary"
     },
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. CTD (Conductivity, Temperature, Depth) casts were undertaken at stations on each of the two R/V Revelle cruises during SPURS-2. These shipboard lowered CTD probes provide continuous conductivity, salinity, and temperature vertical profile observations at fixed sampling locations. There were a total of 50 and 14 CTD casts made during the first and second R/V Revelle cruises respectively, and the data files available here are for continuous CTD profile data for each of the individual casts deployed.  All CTD data were calibrated using shipboard salinometers using IAPSO standard seawater.",
         "license": "proprietary"
     },
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         "description": "The SPURS-2 raindrop ODM-470 disdrometer dataset was collected from the ship during both the 2016 and 2017 cruises. Please see file global attributes and Klepp et al. (2015, 2018) for information on the disdrometer: http://dx.doi.org/10.1016/j.atmosres.2014.12.014, https://doi.org/10.1038/sdata.2018.122 . As explained in the references and global attributes, small drops that cause voltage drops < 0.12 V (i.e. drops with diameters < 0.44 mm) cannot be distinguished from noise by this instrument, and are thus missed. This undercounting of small drops cannot be corrected, and prevents accurate estimation of DSD parameters such as Nw, D0, Dm with any confidence or precision since the minimum detectable drop size is close to the median drop size of tropical oceanic rain (Thompson et al. 2015, https://doi.org/10.1175/JAS-D-14-0206.1). Nonetheless, this dataset provides estimates of drop counts as a function of drop size for the remaining rain drops > 0.44 mm in diameter, and their associated rain rates and liquid water contents. The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. ",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A drifter is a passive Lagrangian sensor platform consisting of a surface buoy and tethered subsurface drogue. Drifter buoys contain GPS/ARGOS and satellite data transmitters, with sensors measuring temperature and other properties. For SPURS-2, a range of drifters were deployed during both Revelle SPURS-2 cruises. These included: standard Surface Velocity Program (SVP) drifters with salinity sensors added (SVP/S), Surface Contact Salinity drifters, CODE, SADOS, AOML and CARTHE-SUPRACT drifters.  For each series, drifter data have been aggregrated within single netCDF data files with their corresponding drifter-IDs and associated near-surface salinity, temperature georeferenced (GPS and ARGOS) trajectory series data.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Neutrally buoyant floats (also known as Mixed Layer Floats - MLF) drift and move through the water column providing continuous CTD temperature and salinity profiles and GPS surface position location data. One float was deployed in SPURS-2 during the first Revelle cruise in August 2016 and recovered in December 2016 after 3.5 months about 1800 km east of the central mooring.  The MLF data are provided in netCDF file format with standards compliant metadata.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Underway physical data from 6 cruises undertaken by the schooner Lady Amber during the SPURS-2 field campaign include along-track meteorological, salinity snake and fixed-hull CTD measurements at 1m and 2 m intake depths. Comparisons with nearby Revelle data facilitate evaluation of uncertainties arising from collecting data from a sailboat, and the characterization of small-scale spatial variability in the ocean and atmosphere. Data files are in netCDF CD/ACDD standards compliant format.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A ship mast meteorological sensor package with an additional set of sophisticated sensors, including a direct covariance flux package was set up on both SPURS-2 Revelle cruises.  These provided georeferenced, along-track atmospheric pressure, temperature, humidity, IR/visible radiation, rain, and wind speed and air-sea flux measurements.  Resulting data are packaged in netCDF files (one per cruise) with standards compliant metadata.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and countercurrent. The SPURS central mooring consisted of a surface meteorological package, surface oceanographic instruments, and subsurface, non-real time oceanographic instruments including CTD, ADCP sensors and point current meters providing continuous series of temperature, salinity and current profile data. Meteorological observations included wind speed, air temperature, precipitation, and radiative flux. The mooring was deployed in 4769 m depth of water on 24 August 2016, at N10:03.0481, W125:01.939, and was recovered on November 11, 2017. WHOI mooring netCDF data files include surface and subsurface time series  of sea temperature, skin temperature, salinity, conductivity, wind velocity, air temperature, relative humidity, precipitation rate, barometric pressure, shortwave and longwave radiation, short/longwave flux, heat Flux, wind Speed and direction.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Two PICO moorings (PMEL 9N and 11N) were deployed on the Revelle cruise in September 2016 in northern and southern domain quadrants at 9deg2.830N, 124deg59.833W and N10:59.0498, W124:57.531 respectively. These moorings contained a surface meteorological package and a \"prawler\", a CTD that crawls up and down the mooring line from 4-450m, yielding time series of salinity and temperature profile data at fixed locations (nominally 8 profiles per day). The moorings were recovered on the second Revelle cruise (Oct. 22 & Nov. 2, 2017). PICO mooring netCDF files contain georeferenced CTD profile data including salinity, temperature, potential temperature, pressure, depth, surface meteorological package data, GPS-Lat/Lon, and profile ID.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Part of the Argo global network of autonomous, self-reporting samplers, Argo floats drift horizontally and move vertically through the water column generally on 10 day cycles, collecting high-quality temperature, conductivity and salinity depth (CTD) profiles from the upper 2000m. Four of the Twenty five floats deployed during SPURS-2 within the campaign spatial domain and time period were additionally equipped with acoustic rain gauges (PAL - Passive Acoustic Listeners). SPURS-2 ARGO-PAL data files are in netCDF/CF-compliant data format and organized per float.  Float identifiers associated with ARGO CTD data are referenced in the metadata of the related PAL files.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The SEA-POL rain radar instrument was employed over the period 22 Oct.-10 Nov. 2017 during the second SPURS-2 R/V Revelle cruise. SEA-POL (seagoing-polarimetric radar) is a C-band, Doppler polarimetric radar system providing 240-degree sector coverage centered on the ships bow via its 1-degree beam width antenna. SEA-POL was used primarily to map rainfall in SPURS-2. The resulting dataset is a series of gridded netCDF data files for a 20 day period at 5-20 minute intervals comprised of rain rate and rain accumulation fields.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. A Rawinsonde is a helium balloon carrying meteorological instruments and a radar target, enabling the velocity of atmospheric parameters to be measured.  During the first Revelle cruise, rawinsondes were launched every 6-hours, providing a total of 85 profiles of temperature, humidity, wind speed and direction through the marine atmospheric boundary layer within the SPURS-2 domain. Similarly, during the second Revelle cruise, rawinsondes were deployed four-times daily within the study area over the 3-week period.  SPURS2 rawinsonde data are available as netCDF, CF-compliant data files.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Two saildrones were deployed over a month period during the second SPURS-2 R/V Revelle cruise in 2017. Saildrone is a state-of-the-art, remotely guided, wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months. It is equipped with a suite of instruments and sensors providing high quality, georeferenced, near real-time, multi-parameter surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. Saildrone data files are in netCDF format and CF/ACDD/NCEI compliant. They contain the saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The Salinity Snake (SS) measures sea surface salinity in the top 1 - 2 cm of the water column, which is the radiometric depth of L-Band satellite radiometers such as on Aquarius/SAC-D, SMAP and SMOS satellites that measure salinity remotely.  The SS consists of four key components: a 10m boom mast, a hose, which is deployed from this boom, a powerful self-priming peristaltic pump which transports a constant stream of a seawater/air emulsion, and a shipboard apparatus, which filters, de-bubbles, sterilizes and analyses the salinity of the water.  The SS was deployed during both SPURS-2 Revelle cruises.  SS data series are provided in netCDF file format, one per cruise.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The towed Surface Salinity Profiler (SSP) platform is a converted paddleboard with a keel and surfboard outrigger that is tethered to the ship and skims the sea surface beyond the ships wake. Below the paddleboard are salinity and temperature sensors at depths of 10, 30, 50 and 100cm, and microstructure sensors that measure turbulence. The SSP was deployed 19 times throughout the first SPURS-2 cruise, totaling over 200 hours of measurements, and a further 15 times during the 2017 cruise.  SSP deployment is most informative when there is a rain event leading to near-surface ocean stratification. The SSP then measures how the ocean changes over the periods before, during, and after rain, and how rainwater mixes into the ocean during recovery. All SSP data files are in netCDF format with standards compliant metadata.",
         "license": "proprietary"
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. During both Revelle cruises, continuous measurements of the partial pressure of CO2 (pCO2), dissolved inorganic carbon (DIC), and pH at surface (0m) and 5m depths were made on water pumped continuously from the Salinity Snake and the ship's intake port.  In addition to these measurements, observational data from the salinity snake and thermosalinograph also include water temperature and salinity time series at the same depths. The temporal resolution of the observations range from 3 seconds (pH) to 3 minutes (DIC). All pCO2 and associated underway data comprising this dataset are in netCDF file format with standards compliant metadata.  Due to issues with the quality of the 2016 underway data, only the data file for the 2017 cruise is available.",
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Underway surface profiling systems (USPS) are automated measurement systems coupled to a research vessels water intake and GPS systems. They provide continuous, along-track surface temperature and salinity measurements at depths of 2, 3 and 5 m using through-hull ports in the bow of the ship. Both SPURS-2 cruises had USPS and associated thermosalinograph (TSG) instrumentation, with measurements calibrated against onboard salinometers. There is one USPS netCDF containing the complete series for each of the 2 cruises.",
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         "description": "The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent.  The WaMoS wave radar instrument was available during the second R/V Revelle cruise of SPURS-2.  WaMoS is a radar-based wave and surface current monitoring system providing wave field imagery and station time series or along track data series for key wave parameter in near near-real time. The single resulting SPURS-2 WaMos data file contains along track wave measurement from the R/V Revelle over the duration of this cruise (5 Oct. to 16 Nov. 2017) for the following essential wave field parameters: wave period, wave length, and wave direction, as well as surface current speed and direction.",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) number product.      Ancillary one-byte (0 to 255) \u201cNUM\u201d (number) files were produced for NASA SRTM Version 3. These files have names corresponding to the elevation files, except with the extension \u201c.NUM\u201d (such as N37W105.NUM). The elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.HGT). The separate NUM file indicates the source of each DEM pixel; the number of ASTER scenes used (up to 100), if ASTER; and the number of SRTM data takes (up to 24), if SRTM. The NUM file for both 3 arc second products (whether sampled or averaged) references the 3 x 3 center pixel.  Note that NUMs less than 6 are water and those greater than 10 are land. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf).    The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.    The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass.    Improvements/Changes from Previous Versions   * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).  ",
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         "license": "proprietary"
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using averaging methods. (See Figure 3 in the User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf).     The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.    The SRTMGL3 data were generated from SRTM1GL (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003) data that fall within that tile. These elevation files use the extension \u201c.HGT\u201d, meaning height (such as N37W105.SRTMGL3.HGT). The primary goal of creating the Version 3 data was to eliminate gaps, or voids, that were present in earlier versions of SRTM data. In areas with limited data, existing topographical data were used to supplement the SRTM data to fill the voids. The source of each elevation pixel is identified in the corresponding SRTMGL3N (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3N.003) product (such as N37W105.SRTMGL3N.NUM).    The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ",
         "license": "proprietary"
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         "license": "proprietary"
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@@ -168332,7 +166187,7 @@
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) SRTM, which includes the global 3 arc second (~90 meter) product. The 3 arc second data was derived from the 1 arc second using sampling and averaging methods. SRTMGL3_NUMNC is used along with the SRTMGL3_NC data product and offers the number count in NetCDF.  NASA Shuttle Radar Topography Mission (SRTM) datasets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. The purpose of SRTM was to generate a near-global digital elevation model (DEM) of the Earth using radar interferometry. SRTM was a primary component of the payload on the Space Shuttle Endeavour during its STS-99 mission. Endeavour launched February 11, 2000 and \ufb02ew for 11 days.   SRTM collected data in swaths, which extend from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km). These swaths are ~225 km wide, and consisted of all land between 60\u00b0 North (N) and 56\u00b0 South (S) latitude. This accounts for about 80% of Earth\u2019s total landmass.  Improvements/Changes from Previous Versions  * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED). ",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) combined (merged) image data product. (See User Guide (https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.2.)  The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information.  The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.  The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass.   Improvements/Changes from Previous Versions  * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects)) Shuttle Radar Topography Mission (SRTM), which includes the global 1 arc second (~30 meter) swath (raw) image data product. (See [User Guide](https://lpdaac.usgs.gov/documents/179/SRTM_User_Guide_V3.pdf) Section 2.2.1)  The SRTM swath image data set consists of radar image files containing brightness values, as well as quality assurance (incidence angle) files for each of four overlapping sub-swaths that passes through a 1 degree by 1 degree tile.  Data from each sub-swath is included as a separate file. Some files may contain only partial data; however, every image pixel acquired by SRTM is included in this data set.  The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.  The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass.    Improvements/Changes from Previous Versions  * Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).",
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         "description": "The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs)(https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Shuttle Radar Topography Mission (SRTM), which includes the Water Body Data Shapefiles and Raster Files (~30 m) product. Version 3.0 contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data (SWBD), in shapefile and rasterized formats.  The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the NGA (previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and \ufb02ew for 11 days.  The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60\u00b0 N and 56\u00b0 S latitude to account for 80% of Earth\u2019s total landmass. ",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "Cycle average and aggregation of lake pass data within predefined hydrological basins. Basin for each cycle. Available in Shapefile file format.",
         "license": "proprietary"
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         "description": "Shapefiles of lakes identified in prior lake database and detected features not in the prior river or lake databases. Lake attributes include water surface elevation, area, derived storage change. Full swath covering individual continents for each half orbit. Available in Shapefile file format.",
         "license": "proprietary"
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         "description": "The SWOT Level 2 Lake Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025. <br>  Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a PLD-oriented feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)).  These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.<br>  Please note that this collection contains SWOT Version C science data products.<br>  This dataset is the parent collection to the following sub-collections: <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_2.0 <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_2.0 <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_2.0 <br>",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025. <br>  Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats.<br>  Please note that this collection contains SWOT Version C science data products. <br>  This dataset is the parent collection to the following sub-collections: <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0 <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0 <br>",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "Cycle average and aggregation of river reach pass data within predefined hydrological basins. Basin for each cycle. Available in Shapefile file format.<br>  Please note that this collection contains SWOT Version C science data products.",
         "license": "proprietary"
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         "description": "Shapefiles of river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in prior river database. Reach attributes include water surface elevation, slope, width, derived discharge. Full swath covering individual continents for each half orbit. Available in Shapefile file format.",
         "license": "proprietary"
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         "description": "The SWOT Level 2 River Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the \"calibration\" or \"fast-sampling\" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the \"science\" phase of the mission, which is expected to continue through 2025. <br>  Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.<br>  Please note that this collection contains SWOT Version C science data products. <br>  This dataset is the parent collection to the following sub-collections: <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_2.0 <br>  https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_reach_2.0 <br>",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset provides the conductivity, temperature and depth (CTD) measurements from the fixed-depth CTD sensors mounted on a full-depth mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. These fixed-depth CTDs cover the full depth from the ocean surface to the bottom. The surface buoy is equipped with a Global Positioning System (GPS) https://doi.org/10.5067/SWTPR-GPS01. There is also an adjacent bottom pressure recorder https://doi.org/10.5067/SWTPR-BPR01. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder. Details can be found in the user guide and the journal reference given in the documentation section.",
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         "description": "This dataset provides the conductivity, temperature and depth (CTD) measurements from the CTD sensors on a WireWalker profiler on a full-depth mooring deployed by the SWOT prelaunch field campaign. The campaign was designed to test the performance of several instruments/platforms in meeting the SWOT Calibration/Validation (CalVal) requirement. It was conducted near the SWOT CalVal crossover location, about 300 kilometers west of Monterey, California between September, 2019 and January, 2020. The WW samples the upper 500 m of the water column, while the deep ocean below 500 m are measured by fixed-depth CTDs https://doi.org/10.5067/SWTPR-CTD01. The campaign also deployed another two CTD moorings, a slocum glider, one bottom pressure recorder and one Pressure Inverted Echo Sounder.  Details can be found in the user guide and the journal reference given in the documentation section.",
         "license": "proprietary"
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         "license": "proprietary"
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIiwidW1tIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIn0%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1_1.0",
         "description": "This dataset contains a simulated lake product to be provided by the Surface Water and Ocean Topography (SWOT) mission with a focus on the North America continent. The product is derived from the high-rate (HR) measurements produced by the SWOT main instrument, a Ka-band Radar Interferometer. These data are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product consists of three shapefiles: 1) an observation-oriented shapefile of lakes identified in the Prior Lake Database (PLD); 2) a PLD-oriented shapefile of lakes identified in the PLD; 3) a shapefile of unassigned features that have not been identified as a lake in the PLD nor as a river in the Prior River Database (PRD). Lake attributes include water surface elevation, area, and uncertainty estimates. The identified lake shapes inherit the SWOT swath width that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
         "license": "proprietary"
     },
@@ -170698,7 +168553,7 @@
         "bbox": "-113, 24, -82, 52",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383657-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383657-POCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIiwidW1tIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIn0%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1_1.0",
         "description": "This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. This product is complementary to the L2_HR_PIXC_V1 product. It provides a less noisy, height-constrained geolocation (latitude, longitude, and height) of the L2_HR_PIXC_V1 pixels. In addition, this product provides an identifier associated with each pixel. The identifier contains the information of the river and/or lake features pulled from the Prior River Database (PRD) or in the Prior Lake Database (PLD). Note that this is a simulated SWOT product and not suited for any scientific exploration.",
         "license": "proprietary"
     },
@@ -170711,7 +168566,7 @@
         "bbox": "-113, 24, -82, 52",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383386-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383386-POCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIiwidW1tIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIn0%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0",
         "description": "This dataset includes simulated water surface elevations that resemble the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. The primary product contains the following: 1. Geolocated elevations (latitude, longitude, and height) 2. Classification mask (water/land flags, and water fraction) 3. Surface areas (projected pixel area on the ground) 4. Relevant data needed to compute and aggregate height and area uncertainties. Additional information includes: 1. Meta data (global instrument parameters) 2. Time varying parameters (TVP), which include sensor position, velocity, altitude, and time 3. Noise power estimates 4. Quality flags 5. Interferogram measurements (power and phase) and range and azimuth indices 6. Geophysical and crossover-calibration correction values. These additional fields are provided to improve the utility of the product and to facilitate generation of downstream products. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
         "license": "proprietary"
     },
@@ -170724,7 +168579,7 @@
         "bbox": "-113, 24, -82, 52",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383790-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2263383790-POCLOUD.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIiwidW1tIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIn0%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0",
         "description": "This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. This is a derived product through resampling the upstream dataset L2_HR_PIXC_V1 and L2_HR_PIXCVEC_V1 onto a uniform grid over the North America continent. A uniform grid is superimposed onto the pixel cloud from the source products, and all pixel-cloud samples within each grid cell are aggregated to produce a single value per raster cell. The raster data are produced geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Note that this is a simulated SWOT product and not suited for any scientific exploration.  ",
         "license": "proprietary"
     },
@@ -170737,7 +168592,7 @@
         "bbox": "-113, 24, -82, 52",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2263384307-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2263384307-POCLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBsZXZlbCAyIHdhdGVyIG1hc2sgcGl4ZWwgY2xvdWQgYXV4aWxpYXJ5IGRhdGEgcHJvZHVjdCwgdmVyc2lvbiBjXCIsXCJQT0NMT1VEXCIsXCJTV09UX0wyX0hSX1BJWENWZWNfMi4wXCIsXCIyLjBcIiwyNzk5NDM4MjYwLDIyXSIsInVtbSI6IltcInN3b3QgbGV2ZWwgMiB3YXRlciBtYXNrIHBpeGVsIGNsb3VkIGF1eGlsaWFyeSBkYXRhIHByb2R1Y3QsIHZlcnNpb24gY1wiLFwiUE9DTE9VRFwiLFwiU1dPVF9MMl9IUl9QSVhDVmVjXzIuMFwiLFwiMi4wXCIsMjc5OTQzODI2MCwyMl0ifQ%3D%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0",
+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=eyJqc29uIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIiwidW1tIjoiW1wic3dvdCBtZWRpdW0tYWNjdXJhY3kgb3JiaXQgZXBoZW1lcmlzIChtb2UpXCIsXCJQT0NMT1VEXCIsXCJTV09UX01PRV8xLjBcIixcIjEuMFwiLDIyOTY5ODk0MDEsMjBdIn0%3D/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0",
         "description": "This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.",
         "license": "proprietary"
     },
@@ -170923,19 +168778,6 @@
         "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
         "license": "proprietary"
     },
-    {
-        "id": "SeaWiFS_L1_GAC_2",
-        "title": "OrbView-2 SeaWiFS Level-1A Global Area Coverage (GAC) Data, version 2",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3202004220-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3202004220-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L1_GAC_2",
-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
-    },
     {
         "id": "SeaWiFS_L1_MLAC_1",
         "title": "OrbView-2 SeaWiFS Merged Local Area Coverage (MLAC) Data, version 1",
@@ -170949,32 +168791,6 @@
         "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
         "license": "proprietary"
     },
-    {
-        "id": "SeaWiFS_L1_MLAC_2",
-        "title": "OrbView-2 SeaWiFS Level-1A Merged Local Area Coverage (MLAC) Data, version 2",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3202004252-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3202004252-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L1_MLAC_2",
-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
-    },
-    {
-        "id": "SeaWiFS_L2_GAC_IOP_2022.0",
-        "title": "OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Inherent Optical Properties (IOP) Data, version 2022.0",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3198658845-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3198658845-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L2_GAC_IOP_2022.0",
-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
-    },
     {
         "id": "SeaWiFS_L2_GAC_IOP_R2022.0",
         "title": "OrbView-2 SeaWiFS Regional Global Area Coverage (GAC) Inherent Optical Properties (IOP) Data, version R2022.0",
@@ -170988,19 +168804,6 @@
         "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
         "license": "proprietary"
     },
-    {
-        "id": "SeaWiFS_L2_GAC_OC_2022.0",
-        "title": "OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Ocean Color (OC) Data, version 2022.0",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789774382-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789774382-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L2_GAC_OC_2022.0",
-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
-    },
     {
         "id": "SeaWiFS_L2_GAC_OC_R2022.0",
         "title": "OrbView-2 SeaWiFS Regional Global Area Coverage (GAC) Ocean Color (OC) Data, version R2022.0",
@@ -171014,32 +168817,6 @@
         "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
         "license": "proprietary"
     },
-    {
-        "id": "SeaWiFS_L2_LAND_2022.0",
-        "title": "OrbView-2 SeaWiFS Level-2 Regional Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3113252901-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3113252901-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L2_LAND_2022.0",
-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
-    },
-    {
-        "id": "SeaWiFS_L2_MLAC_IOP_2022.0",
-        "title": "OrbView-2 SeaWiFS Level-2 Regional Merged Local Area Coverage (MLAC) Inherent Optical Properties (IOP) Data, version 2022.0",
-        "catalog": "OB_CLOUD STAC Catalog",
-        "state_date": "1997-09-04",
-        "end_date": "2010-12-11",
-        "bbox": "-180, -90, 180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3198658953-OB_CLOUD.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3198658953-OB_CLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/SeaWiFS_L2_MLAC_IOP_2022.0",
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         "title": "OrbView-2 SeaWiFS Regional Merged Local Area Coverage (MLAC) Inherent Optical Properties (IOP) Data, version R2022.0",
@@ -171053,19 +168830,6 @@
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-        "title": "OrbView-2 SeaWiFS Level-2 Regional Merged Local Area Coverage (MLAC) Ocean Color (OC) Data, version 2022.0",
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-        "description": "The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.",
-        "license": "proprietary"
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         "id": "SeaWiFS_L2_MLAC_OC_R2022.0",
         "title": "OrbView-2 SeaWiFS Regional Merged Local Area Coverage (MLAC) Ocean Color (OC) Data, version R2022.0",
@@ -171079,19 +168843,6 @@
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         "id": "SeaWiFS_L3b_CHL_R2022.0",
         "title": "OrbView-2 SeaWiFS Global Binned Chlorophyll (CHL) Data, version R2022.0",
@@ -171105,32 +168856,6 @@
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         "id": "SeaWiFS_L3b_IOP_R2022.0",
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         "id": "SeaWiFS_L3b_KD_R2022.0",
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@@ -171170,32 +168882,6 @@
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         "id": "SeaWiFS_L3b_PAR_R2022.0",
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@@ -171209,19 +168895,6 @@
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@@ -171235,19 +168908,6 @@
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@@ -171261,32 +168921,6 @@
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@@ -171300,32 +168934,6 @@
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@@ -171339,32 +168947,6 @@
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@@ -171404,32 +168973,6 @@
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@@ -171469,19 +168999,6 @@
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@@ -171495,32 +169012,6 @@
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-        "license": "proprietary"
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         "id": "SeaWiFS_L3m_RRS_R2022.0",
         "title": "OrbView-2 SeaWiFS Global Mapped Remote-Sensing Reflectance (RRS) Data, version R2022.0",
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         "license": "proprietary"
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-        "title": "OrbView-2 SeaWiFS Level-3 Global Mapped Euphotic Depth (ZLEE) Data, version 2022.0",
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         "title": "OrbView-2 SeaWiFS 4B Global Binned Garver-Siegel-Maritorena Model (GSM) Data, version R2022.0",
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         "description": "Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms.   The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "In many uses of multispectral satellite imagery, clouds obscure what we really care about - for example, tracking wildfires, mapping deforestation, or monitoring crop health. Being able to more accurately remove clouds from satellite images filters out interference, unlocking the potential of a vast range of use cases. With this goal in mind, this training dataset was generated as part of [crowdsourcing competition](https://www.drivendata.org/competitions/83/cloud-cover/), and later on was validated using a team of expert annotators. The dataset consists of Sentinel-2 satellite imagery  and corresponding cloudy labels stored as GeoTiffs. There are 22,728 chips in the training data,  collected between 2018 and 2020.",
         "license": "proprietary"
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         "description": "This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pl\u00e9iades imagery.",
         "license": "proprietary"
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         "description": "This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites.",
         "license": "proprietary"
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         "description": "Glacial isostatic adjustment (GIA) is an ongoing geophysical process and is measured by gravimetry satellites like GRACE and GRACE-FO. To isolate signals of contemporary surface mass loss in the cumulative satellite gravimetry measurements, contemporary GIA rates are computed and subtracted from the satellite gravimetry observations. The GIA correction models provided here are filtered such that they are compatible with Level-3 post-processing filters applied to GRACE(-FO) data as indicated in the [product_id]. In this way, user can effectively assess the impact of the applied GIA correction, and substitute different GIA models should that be desired. This GIA dataset is mapped into 0.5-degree global grid compatible with the JPL Mascon solution, provided in netCDF format.",
         "license": "proprietary"
     },
@@ -172596,7 +170074,7 @@
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.1Mv03). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The water storage/height anomalies are given in equivalent water thickness units (cm). The solution provided here is derived from solving for monthly gravity field variations in terms of geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the solution inversion to intrinsically remove correlated error. Thus, these Mascon grids do not need to be destriped or smoothed, like traditional spherical harmonic gravity solutions. The complete Mascon solution consists of 4,551 relatively independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. It should be noted that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines. This RL06.1Mv03 is an updated version of the previous Tellus JPL Mascon RL06Mv02 (DOI, 10.5067/TEMSC-3JC62). RL06.1Mv03 differs from RL06Mv02 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1Mv03 uses the ACH data product. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.1_V3 ",
         "license": "proprietary"
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         "description": "This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions. <br><br> The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines. <br><br> This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.",
         "license": "proprietary"
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         "description": "The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.",
         "license": "proprietary"
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-        "title": "CSR TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.1 version 04",
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-    },
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-        "title": "CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.2 version 04",
-        "catalog": "POCLOUD STAC Catalog",
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-        "license": "proprietary"
-    },
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-        "id": "TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04_RL06.2v04",
-        "title": "CSR TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.2 version 04",
-        "catalog": "POCLOUD STAC Catalog",
-        "state_date": "2018-05-22",
-        "end_date": "",
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         "description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br>    GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.",
         "license": "proprietary"
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         "description": "This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br>    GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 \u2013 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.",
         "license": "proprietary"
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         "description": "!!!Temporary notice posted Sept. 27th, 2024!!! These data are in the process of being ingested and not all files are available yet. The data were made public early to allow assessment by early science users. Accordingly, not all data set resources may be available yet. Please check over the next 2-3 weeks for finalization of this data set and PO.DAAC's release announcement. <br><br> This dataset includes satellite-based observations of calibrated, geo-located antenna temperature and brightness temperatures, along with the sensor telemetry used to derive those values. Brightness temperatures are derived from the microwave band frequencies 87, 164, 174, 178 and 181 GHz. This product is best suited for a cal/val user or sensor expert. These level 1c measurements make up the temperature sensor data record (TSDR) from the TEMPEST (Temporal Experiment for Storms and Tropical Systems) sensor aboard the international space station (ISS), starting in January 2022 forward-streaming to PO.DAAC till the planned mission end in December 2024. TEMPEST swath width is 1400 kilometers and resolution at nadir is 25 km for the 87 GHz channel and 13 km for the 180 GHz channels. Data files in HDF5 format are available at roughly hourly frequency (the ISS orbit period is ~90 minutes), although note that the coverage shown in the thumbnail is for a full day. Files include calibration and flag data in addition to brightness temperatures. Version 10.0 is the first public release, and is named as such to be consistent with the internal version numbering of the project team prior to release. <br><br> The TEMPEST instrument is a microwave radiometer deployed as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission, with the primary objective of tropical cyclone intensity tracking. It operates nominally on-orbit aboard the ISS and data are non-sun-synchronous. A successful mission will demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data. TEMPEST was provided by the Jet Propulsion Laboratory and flown by the United States Space Force, Space Systems Command, Development Corps for Innovation and Prototyping.",
         "license": "proprietary"
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         "description": "GDS2 Version -The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature  (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, SST and wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial precessing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N.  Remote Sensing Systems has produced a Version-4 TMI ocean SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST)  by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols.  The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"",
         "license": "proprietary"
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         "description": "The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is a well calibrated passive microwave radiometer, similar to the Special Sensor Microwave Imager (SSM/I), that contains lower frequency channels required for sea surface temperature  (SST) retrievals. The TRMM is part of the NASA's mission to planet Earth, and is a joint venture between NASA and the Japan Aerospace Exploration Agency (JAXA) to measure precipitation, water vapor, sea surface temperature (SST) and surface wind in the global tropical regions and was launched in 27 November 1997 from the Tanegashima Space Center in Tanegashima, Japan. The TRMM satellite travels west to east in a 402 km altitude semi-equatorial processing orbit that results in day-to-day changes in the observation time of any given earth location between 38S and 38N.  Remote Sensing Systems (REMSS) has produced a Version-7.1a TMI SST dataset for the Group for High Resolution Sea Surface Temperature (GHRSST) by applying an algorithm to the 10.7 GHz channel through a removal of surface roughness effects. In contrast to infrared SST observations, microwave retrievals can be measured through clouds, which are nearly transparent at 10.7 GHz. Microwave retrievals are also insensitive to water vapor and aerosols.  The algorithm for retrieving SSTs from radiometer data is described in \"AMSR Ocean Algorithm.\"",
         "license": "proprietary"
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         "description": "The Sensory Data Record (SDR) is similar to the GDR product except that it also contains waveforms, which are required for retracking.  This is an expert level product.  If you do not need the waveforms then the GDR should suit your needs.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197672-POCLOUD.html",
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         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Continuous Fields (VCF) Version 1 data product (VCF5KYR) provides global fractional vegetation cover at 0.05 degree (5,600 meter) spatial resolution at yearly intervals from 1982 to 2016. The VCF5KYR data product is derived from a bagged linear model algorithm using Long Term Data Record Version 4 (LTDR V4) data compiled from Advanced Very High Resolution Radiometer (AVHRR) observations. Fractional vegetation cover (FVC) is the ratio of the area of the vertical projection of green vegetation above ground to the total area, capturing the horizontal distribution and density of vegetation on the Earth\u2019s surface. FVC is a primary means for measuring global forest cover change and is a key parameter for a variety of environmental and climate-related applications, including carbon land surface models and biomass measurements. The three bands included in each VCF5KYR Version 1 GeoTIFF are: percent of tree cover, non-tree vegetation, and bare ground. A water mask was applied with all pure water pixels (defined as \u2265 95% water coverage) set to zero.    Data from years 1994 and 2000 were excluded due to lack of data in the LTDR V4.",
         "license": "proprietary"
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         "description": "The VIIRS_N20-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-1 satellite, renamed as NOAA-20 (N20). N20 was launched on November 18, 2017, the 2nd satellite in the US NOAA JPSS series. <br><br>     VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates.  Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 20 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was  upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br>    The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N21 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N21-NAVO-L2P-v3.0) datasets.  It also has similar coverage and quality as the NOAA ACSPO VIIRS  L2P SST (https://podaac.jpl.nasa.gov/dataset/N20-VIIRS-L2P-ACSPO-v2.80).   ",
         "license": "proprietary"
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         "description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available at https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L3U-v2.80, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask. ",
         "license": "proprietary"
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         "description": "NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-STAR-L2P-v2.80. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2847232536-POCLOUD.html",
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         "description": "The VIIRS_N21-NAVO-L2P-v3.0 dataset produced by the Naval Oceanographic Office (NAVO) derives the 1-meter depth Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on November 10, 2022, the 3rd satellite in the US NOAA JPSS series. <br><br>     VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NAVO's Level-2 SST processor version 3.0 (v3.0). Data contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates.  Each netCDF file has 768 x 3200 pixels in size, in compliance with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The data record is available back to Feb. 21 2024. The L2P SST v3.0 is the first release at PO.DAAC derived from the L2P SST processor v3.0, which was  upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.<br><br>    The product is comparable with the NPP VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-NAVO-L2P-v3.0) and the N20 VIIRS L2P (https://podaac.jpl.nasa.gov/dataset/VIIRS_N20-NAVO-L2P-v3.0).  It also has similar coverage and quality as the  NOAA ACSPO VIIRS  L2P SST (https://podaac.jpl.nasa.gov/dataset/N21-VIIRS-L2P-ACSPO-v2.80).      ",
         "license": "proprietary"
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         "description": "These files contain NASA produced skin sea surface temperature (SST) products from the Infrared (IR) channels of the Visible and Infrared Imager/Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. VIIRS is a multi-disciplinary instrument that is also being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, of which NOAA-20 is the first. JPSS is a multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA).  Suomi-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data.  VIIRS has 22 spectral bands ranging from 412 nm to 12 micron .  There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375 m), and one day-night band (DNB). VIIRS uses on-board pixel aggregation to reduce the growth in size of pixels away from nadir. Two SST products are contained in these files. The first is a skin SST produced separately for day and night observations, derived from the long wave IR 11 and 12 micron  wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST products from heritage and current NASA sensors. At night, a second triple channel SST product is generated using the 3.7 , 11 and 12 micron  IR channels, identified as SST_triple. Due to the sun glint in the 3.7 micron  SST_triple can only be used at night.  VIIRS L2P SST data have a 750 spatial resolution at nadir and are stored in ~288 five minute granules per day. Full global coverage is obtained each day. The production of VIIRS NASA L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS were responsible for sea surface temperature algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of VIIRS ocean products. JPL acquires VIIRS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. In mid-August, 2018, the RSMAS involvement in the VIIRS SST project ceased, and the subsequent fields are not maintained.The R2016.2 supersedes the previous v2016.0 datasets which can be found at  https://doi.org/10.5067/GHVRS-2PN16",
         "license": "proprietary"
     },
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011.  The VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath.  However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
     },
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite launched on 28 October 2011.  The VIIRS instrument is a a 22-band, multi-spectral scanning radiometer with a 3040-km swath width that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 740 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath.  However, the processing of this dataset aggregates two pixels into one so the resolution is 1500 meters at nadir. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
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         "description": "A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth. The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath.  This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades.  It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates.  Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO NPP/VIIRS L2P product available here https://podaac.jpl.nasa.gov/dataset/VIIRS_NPP-STAR-L2P-v2.80 . The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://www.doi.org/10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes.     The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes.   The VIP15 VI data product is provided twice monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP15 VI product contains 12 Science Datasets (SDS) which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.  The VIP15 dataset includes two composites per month. The first composite is generated from day 1 to 15, and the second composite includes the remaining days of the month. This dataset consists of 24 files per year.",
         "license": "proprietary"
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         "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.    The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI.     Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VJ113C1 product: EVI and NDVI.",
         "license": "proprietary"
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         "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires (VJ114IMG) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 375 meter resolution from the VIIRS sensor aboard the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as thermal anomalies.     The VJ114IMG product includes 26 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., radiance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114IMG product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes.    Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114IMG product is also used to generate higher-level fire data products.    Use of the VJ103MODLL data product is required to apply accurate geolocation information to the VJ114IMG Science Datasets (SDS).    ",
         "license": "proprietary"
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         "description": "The Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Thermal Anomalies (VJ114) Version 2 product is produced in 6-minute temporal satellite increments (swaths) at 750 meter resolution from the VIIRS sensor located on the NOAA-20 satellite. This product is designed after the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire data products to promote the continuity of the Earth Observation System (EOS) mission. This data product can enable users to understand the location and intensity of fire events as well as identifying thermal anomalies.     The VJ114 product includes 31 science dataset layers to analyze key factors in fire detection, including atmospheric conditions (e.g., atmospheric reflectance, solar zenith angle, brightness temperature) and fuel type for the event. The fire mask layer in the VJ114 product is the primary layer and can be used to identify fires and other thermal anomalies such as volcanoes. In addition to the fire mask, brightness temperature is provided for VIIRS channels M5, M7, M11, M13, M15, and M16.      Each swath of data is approximately 3,060 kilometers along track (long) and 3,060 kilometers across track (wide). The VJ114 product is also used to generate higher-level fire data products.    Use of the (VJ103MODLL) (https://doi.org/10.5067/viirs/vj103modll.021) data product is required to apply accurate geolocation information to the VJ114 Science Datasets (SDS).  ",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VJ121C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VJ121C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VJ121A1D (http://doi.org/10.5067/VIIRS/VJ121A1D.002) and VJ121A1N (http://doi.org/10.5067/VIIRS/VJ121A1N.002) daily acquisitions from the monthly period. Unlike the VJ121A1 data sets where the daytime and nighttime acquisitions are separate products, the VJ121C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).  ",
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         "license": "proprietary"
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         "description": "This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI).  VIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The 8-day Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Surface Reflectance (VNP09H1) Version 1 composite product provides an estimate of land surface reflectance from the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS sensor for three imagery bands (I1, I2, I3) at nominal 500 meter resolution (~463 meter). The 500 meter dataset is derived through resampling the native 375 meter VIIRS resolution in the L2 input product. The data are corrected for atmospheric conditions such as the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. Each pixel represents the best possible Level 2G observation during an 8-day period, which is selected on the basis of high observation coverage, low sensor angle, the absence of clouds or cloud shadow, and aerosol loading. The three reflectance bands, this product includes a state quality assurance (QA) layer and a reflectance band quality layer.     ",
         "license": "proprietary"
     },
@@ -196932,7 +194345,7 @@
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         "license": "proprietary"
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         "description": "This data set contains daily 'cloud-free' snow cover produced from the VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 1 snow cover product. A cloud-gap-filled algorithm is utilized to replace \u2018cloud-covered\u2019 pixels with \u2018cloud-free pixels\u2019 for the purpose of estimating the snow cover that may exist under current cloud cover. The data are provided daily and mapped to a 375 m sinusoidal grid.",
         "license": "proprietary"
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         "description": "This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.",
         "license": "proprietary"
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         "description": "This data set contains daily snow cover derived from radiance data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. The data is a gridded composite, generated from 6 minute swaths, and projected to a 375 m Sinusoidal grid. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections.",
         "license": "proprietary"
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2317040766-NSIDC_ECS.html",
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         "description": "This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05\u00b0 (approx. 5 km) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/45VDCKJBXWEE).",
         "license": "proprietary"
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         "description": "This data set reports the location of snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.",
         "license": "proprietary"
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         "description": "This data set reports snow cover using radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Snow cover is identified using the Normalized Difference Snow Index (NDSI) and a series of quality control screens.",
         "license": "proprietary"
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@@ -197075,7 +194488,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1392010616-LPDAAC_ECS.html",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission.    The VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI),the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI.     Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI.",
         "license": "proprietary"
     },
@@ -197088,7 +194501,7 @@
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Night Version 2 product (VNP21A1N) is compiled daily from nighttime Level 2 Gridded (L2G) intermediate products.      The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given night. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.    The VNP21A1N product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A1N) (https://doi.org/10.5067/MODIS/MOD21A1N.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).    The VNP21A1N product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1N granule.  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.001) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.001) products over an 8-day compositing period into a single product.    The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file.    The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.006) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.    The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule.  ",
         "license": "proprietary"
     },
@@ -197400,7 +194813,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) 8-day product (VNP21A2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product.    The VNP21A2 dataset is an 8-day composite LST&E product at 1 kilometer resolution that uses an algorithm based on a simple-averaging method. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21A2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file.    The VNP21A2 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21A2) (https://doi.org/10.5067/MODIS/MOD21A2.061) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).    The VNP21A2 product contains 11 Science Datasets (SDS): LST, quality control, view zenith angle, and time of observation for both day and night observations along with emissivity for bands M14, M15, and M16. Low-resolution browse images for day and night LST are also available for each VNP21A2 granule.  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) Climate Modeling Grid Version 2 product (VNP21C) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. The L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.002) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The 0.05 degree (5600 m) dataset is derived through resampling the native 750 meter VIIRS resolution in the input product. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) 8-day Climate Modeling Grid Version 2 product (VNP21C2) combines the daily (VNP21A1D) (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and (VNP21A1N) (http://doi.org/10.5067/VIIRS/VNP21A1N.002) products over an 8-day compositing period into a single product. The VNP21C2 dataset is an 8-day composite LST&E product at 0.05 degree (~5,600 meter) resolution that uses an algorithm based on a simple-averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud-free VNP21A1D and VNP21A1N daily acquisitions from the 8-day period. Unlike the VNP21A1 datasets where the daytime and nighttime acquisitions are separate products, the VNP21C2 contains both daytime and nighttime acquisitions as separate science dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Land Surface Temperature and Emissivity (LST&E) monthly Climate Modeling Grid Version 2 product (VNP21C3) provides LST&E by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (~5,600 meter) resolution. The VNP21C3 dataset is a monthly composite LST&E product that uses an algorithm based on a simple averaging method and is formatted as a CMG for use in climate simulation models. The algorithm calculates the average from all the cloud free VNP21A1D (http://doi.org/10.5067/VIIRS/VNP21A1D.002) and VNP21A1N (http://doi.org/10.5067/VIIRS/VNP21A1N.002) daily acquisitions from the monthly period. Unlike the VNP21A1 data sets where the daytime and nighttime acquisitions are separate products, the VNP21C3 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 1 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.    The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.006) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).      Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule.  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Version 2 swath product (VNP21) is produced daily in 6-minute temporal increments of satellite acquisition. The VNP21 product uses a physics-based algorithm to dynamically retrieve both the LST and emissivity simultaneously for VIIRS thermal infrared bands M14 (8.55 \u00b5m), M15 (10.76 \u00b5m), and M16 (12 \u00b5m) at a spatial resolution of 750 meters.    The VNP21 product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6.1 product (MOD21) (https://doi.org/10.5067/MODIS/MOD21.061) using the same input atmospheric products and algorithmic approach based on the ASTER Temperature Emissivity Separation (TES) technique. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. VIIRS LST&E products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf).    Provided in the VNP21 product are layers for LST, quality control, emissivity for bands M14, M15, and M16, LST&E errors, view angle, ASTER Global Emissivity Dataset (GED), Precipitable Water Vapor (PWV), ocean-land mask, latitude, and longitude. A low-resolution browse image for LST is also available for each VNP21 granule.  ",
         "license": "proprietary"
     },
@@ -197504,7 +194917,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1712040022-LPDAAC_ECS.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1712040022-LPDAAC_ECS.html",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22C2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 0.05 degree (~5,600 meters) spatial resolution are identified for up to two detected growing cycles per year.     Provided in each VNP22C2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22C2 granule.    ",
         "license": "proprietary"
     },
@@ -197517,7 +194930,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year.    Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule.      ",
         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI).  VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.",
         "license": "proprietary"
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         "description": "This data set estimates of sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.  VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.",
         "license": "proprietary"
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         "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.  VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.",
         "license": "proprietary"
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         "description": "This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.  VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C1) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C1 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. VNP43C1 supplies the weighting parameters associated with the RossThick/Li-Sparse-Reciprocal BRDF model that best describes the anisotropy of each pixel, which is used to produce the VNP43C3 Albedo and VNP43C4 Nadir BRDF-Adjusted Reflectance (NBAR) products. The highest quality full inversion values are used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the ancillary layers. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models.     The VNP43C1 product includes 39 layers containing the three parameters (fiso, fvol, and fgeo) for the VIIRS Day/Night band (DNB), moderate resolution bands M1 through M5, M7, M8, M10, and M11, as well as the shortwave band, visible band, and near-infrared (NIR) broadbands. Along with the parameter data for the 13 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format.  ",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance Daily Global 0.05 Degree Climate Modeling Grid (CMG) Version 1 product (VNP43C4) is derived from the 30 arc second CMG VNP43D Version 1 product suite. VNP43C3 is generated daily from all available high-quality reflectance data over a 16-day moving window emphasizing the ninth day of the retrieval period, which is reflected in the Julian date in the filename. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43C1 to compute NBAR values for the VIIRS Day/Night band (DNB), and moderate resolution bands M1 through M5, M7, M8, M10, and M11. The algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. The quality and inversion status of the majority of the underlying 30 arc second pixels is provided as well as the percentage of the underlying pixels that were present or were snow covered. Users are encouraged to assess the quality information before using the BRDF/Albedo data. This 0.05 degree (5,600 meters at the equator) CMG product covers the entire globe for use in climate simulation models.     The VNP43C3 product includes 10 layers containing NBAR values for VIIRS DNB and moderate resolution bands M1 through M5, M7, M8, M10, and M11. Along with the NBAR data for the 10 bands are five ancillary layers for uncertainty, quality, local solar noon, percent finer resolution inputs, and snow cover. A low-resolution browse image is also available showing NBAR bands M5, M4, and M3 as a red, green, blue (RGB) image in JPEG format.  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M1 product (VNP43D01) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1(https://doi.org/10.5067/VIIRS/VNP43MA1.001)  product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D01 is the BRDF isotropic parameter for VIIRS band M1 (0.412 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.",
         "license": "proprietary"
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@@ -197738,7 +195151,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M1 product (VNP43D02) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D02 is the BRDF volumetric parameter for VIIRS band M1 (0.412 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M1.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M3 product (VNP43D09) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D09 is the BRDF geometric parameter for VIIRS band M3 (0.488 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M3.  ",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M5 product (VNP43D15) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D15 is the BRDF geometric parameter for VIIRS band M5 (0.672 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M5.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M7 product (VNP43D16) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D16 is the BRDF isotropic parameter for VIIRS band M7 (0.865 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M7.    ",
         "license": "proprietary"
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@@ -197933,7 +195346,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M8 product (VNP43D20) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D20 is the BRDF volumetric parameter for VIIRS band M8 (1.240 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8.    ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M8 product (VNP43D21) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D21 is the BRDF geometric parameter for VIIRS band M8 (1.240 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M8.  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.",
         "license": "proprietary"
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@@ -198011,7 +195424,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M10 product (VNP43D23) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D23 is the BRDF volumetric parameter for VIIRS band M10 (1.61 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.    ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M10 product (VNP43D24) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D24 is the BRDF geometric parameter for VIIRS band M10 (1.61 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M11 product (VNP43D25) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D25 is the BRDF isotropic parameter for VIIRS band M11 (2.25 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.",
         "license": "proprietary"
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@@ -198050,7 +195463,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 Band M11 product (VNP43D26) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document [ATBD).    VNP43D26 is the BRDF volumetric parameter for VIIRS band M11 (2.25 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Band M11 product (VNP43D27) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D27 is the BRDF geometric parameter for VIIRS band M11 (2.25 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for VIIRS band M11.  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 VIS product (VNP43D28) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D28 is the BRDF isotropic parameter for the VIIRS visible broadband (0.64 \u03bcm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.  ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 2 VIS product (VNP43D29) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D29 is the BRDF volumetric parameter for the VIIRS visible broadband (0.64 \u03bcm). The volumetric parameter, in conjunction with the isotropic and geometric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.    ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 VIS product (VNP43D30) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D30 is the BRDF geometric parameter for the VIIRS visible broadband (0.64 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS visible broadband.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 3 Day-Night Band (DNB) product (VNP43D39) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D39 is the BRDF geometric parameter for the VIIRS DNB (0.7 \u03bcm). The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the VIIRS DNB.    ",
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         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M2 product (VNP43D43) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2](https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD).     VNP43D43 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M2.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Valid Observation Band M3 product (VNP43D44) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD).     VNP43D44 contains the valid observation quality layer representing each of the 16 days of the retrieval period for VIIRS band M3.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Snow Status product (VNP43D52) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD).     VNP43D52 contains the snow status quality layer, which identifies each pixel as either \u201cSnow-free Albedo Retrieved\u201d or \u201cSnow Albedo Retrieved\u201d for the acquisition period.  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Uncertainty product (VNP43D53) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer for each of the parameters included in the VNP43MA2 (https://doi.org/10.5067/VIIRS/VNP43MA2.001) product. VNP43D40 through VNP43D53 are the 30 arc second BRDF/Albedo Quality values, the Local Solar Noon values, the Valid Observations of the moderate resolution bands (M1 through M5, M7, M8, M10, and M11) plus the Day/Night Band (DNB), the Snow Status, and the Uncertainty. Details regarding methodology are available on the VNP43MA2 product page and in the Algorithm Theoretical Basis Document (ATBD).     VNP43D53 contains the uncertainty range of each BRDF/Albedo pixel for the retrieval period.   ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M1 (VNP43D54) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D54 is the BSA for VIIRS band M1 (0.412 \u03bcm).   ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for Band M4 (VNP43D57) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D57 is the BSA for VIIRS band M4 (0.555 \u03bcm). ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for NIR (VNP43D64) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D64 is the BSA for the VIIRS NIR broadband (0.865 \u03bcm).  ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M4 (VNP43D70) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D70 is the WSA for VIIRS band M4 (0.555 \u03bcm).   ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M5 (VNP43D71) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.       VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D71 is the WSA for VIIRS band M5 (0.672 \u03bcm). ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band M7 (VNP43D72) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D72 is the WSA for VIIRS band M7 (0.865 \u03bcm). ",
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         "license": "proprietary"
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         "license": "proprietary"
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         "license": "proprietary"
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@@ -198700,7 +196113,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo white-sky albedo for band VIS (VNP43D76) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible (VIS), near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D76 is the WSA for the VIIRS visible broadband (0.64 \u03bcm).   ",
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         "license": "proprietary"
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@@ -198778,7 +196191,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for Band M11 (VNP43D88) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D88 is the NBAR for VIIRS band M11 (2.25 \u03bcm).   ",
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1607364966-LPDAAC_ECS.html",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Nadir BRDF-Adjusted Reflectance (NBAR) for DNB (VNP43D89) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer.     VNP43D80 through VNP43D89 are the NBAR products of the VNP43D BRDF/Albedo product suite. NBAR values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) included in the VNP43MA4 (https://doi.org/10.5067/VIIRS/VNP43MA4.001) product. In addition to the bands included in VNP43MA4, this product suite includes NBAR values for the VIIRS Day/Night Band (DNB). The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. Details regarding methodology are available on the VNP43MA4 product page and in the Algorithm Theoretical Basis Document (ATBD).    VNP43D89 is the NBAR for VIIRS DNB (0.7 \u03bcm).   ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43DNBA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43DNBA1 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).    The VNP43DNBA1 data product provides two SDS layers for mandatory quality and model parameters representing fiso, fvol, and fgeo for the VIIRS DNB. A low-resolution browse is also available showing BRDF/Albedo parameters for the DNB as a red, green, blue (RGB) image in JPEG format.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43DNBA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43DNBA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43DNBA2 product gives information regarding band quality and days of valid observation within a 16-day period for the VIIRS DNB. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).    The VNP43DNBA2 data product provides a total of seven SDS layers, including BRDF/Albedo band quality and days of valid observation within a 16-day period for the VIIRS DNB, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name.    ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43DNBA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43DNBA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).    The VNP43DNBA3 product provides BSA, WSA, and mandatory quality layers for the VIIRS DNB. A low-resolution image is also available showing retrievals of WSA for the shortwave broadband in JPEG format.",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNB4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).    The VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format.    ",
         "license": "proprietary"
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 1 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth\u2019s surface. All VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.     The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).    The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.   ",
         "license": "proprietary"
     },
@@ -198973,7 +196386,7 @@
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.     The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).    The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name.   ",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).    The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format.",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.     The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf).    The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format.",
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         "description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA\u2019s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite.    The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD).    The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format.    Product Maturity    Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1632559364-LPDAAC_ECS.html",
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         "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.     VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/NPP_QA/NPPpage.cgi?fileName=maturity&subdir=forPage) data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.    The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.    Notification:  VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas.    ",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2847928673-LPCLOUD.html",
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-        "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.   VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.  The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.  Notification:  VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas.  ",
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+        "description": "The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1\u2013366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.   The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. ",
         "license": "proprietary"
     },
     {
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         "description": "Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich.",
         "license": "proprietary"
     },
@@ -199766,7 +197179,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197620-POCLOUD.html",
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         "description": "Contains three parameters: ocean near-surface wind speed, columnar water vapor, and columnar liquid water. Product is produced by Frank Wentz at Remote Sensing Systems using data obtained from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR). Observations within 100 km of land are excluded; ice flags are also utilized. Data is obtained from all 10 individual SMMR channels, which closely correspond to the SMM/I channels and utilizing the same processing algorithms that were used to produce similar products derived from SSM/I observations (see PO.DAAC products 33 and 34).",
         "license": "proprietary"
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@@ -199779,7 +197192,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2617197621-POCLOUD.html",
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         "description": "Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality.",
         "license": "proprietary"
     },
@@ -199974,7 +197387,7 @@
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2253727823-NSIDC_ECS.html",
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         "description": "This Western United States snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow-covered area (fSCA) and snow depth (SD) at 16 arc-second (~500 m) resolution from water years 1985 to 2021. This data set was developed to be compared to SnowEx data sets but its utility reaches beyond that since its spatial and temporal bounds extend over the entire Western U.S. and over several decades.",
         "license": "proprietary"
     },
@@ -200156,7 +197569,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2674700048-LPCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2674700048-LPCLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvdGVycmErYXF1YSBicmRmL2FsYmVkbyB3aGl0ZSBza3kgYWxiZWRvIHZpcyBkYWlseSBsMyBnbG9iYWwgMzBhcmNzZWMgY21nIHYwNjFcIixcIkxQQ0xPVURcIixcIk1DRDQzRDU5XCIsXCI2MVwiLDI1NDAyNzMxODMsMjBdIiwidW1tIjoiW1wibW9kaXMvdGVycmErYXF1YSBicmRmL2FsYmVkbyB3aGl0ZSBza3kgYWxiZWRvIHZpcyBkYWlseSBsMyBnbG9iYWwgMzBhcmNzZWMgY21nIHYwNjFcIixcIkxQQ0xPVURcIixcIk1DRDQzRDU5XCIsXCI2MVwiLDI1NDAyNzMxODMsMjBdIn0%3D/WaterBalance_Monthly_Historical_GRIDMET_1.5",
+        "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections?cursor=eyJqc29uIjoiW1wibW9kaXMvdGVycmErYXF1YSBicmRmL2FsYmVkbyB3aGl0ZSBza3kgYWxiZWRvIHZpcyBkYWlseSBsMyBnbG9iYWwgMzBhcmNzZWMgY21nIHYwNjFcIixcIkxQQ0xPVURcIixcIk1DRDQzRDU5XCIsXCI2MVwiLDI1NDAyNzMxODMsMjFdIiwidW1tIjoiW1wibW9kaXMvdGVycmErYXF1YSBicmRmL2FsYmVkbyB3aGl0ZSBza3kgYWxiZWRvIHZpcyBkYWlseSBsMyBnbG9iYWwgMzBhcmNzZWMgY21nIHYwNjFcIixcIkxQQ0xPVURcIixcIk1DRDQzRDU5XCIsXCI2MVwiLDI1NDAyNzMxODMsMjFdIn0%3D/WaterBalance_Monthly_Historical_GRIDMET_1.5",
         "description": "This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model.  Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format.",
         "license": "proprietary"
     },
@@ -200221,7 +197634,7 @@
         "bbox": "-123.5313889, 28.3, -93.8227778, 48.4136111",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412788-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412788-MLHUB.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/MLHUB/collections?cursor=eyJqc29uIjoiW1wicmFtcCBidWlsZGluZyBmb290cHJpbnQgZGF0YXNldCAtIG1lc29wb3RhbWlhLCBzdC4gdmluY2VudFwiLFwiTUxIVUJcIixcInJhbXAgQnVpbGRpbmcgRm9vdHByaW50IERhdGFzZXQgLSBNZXNvcG90YW1pYSwgU3QuIFZpbmNlbnRcIixcIjFcIiwyNzgxNDEyMjU2LDldIiwidW1tIjoiW1wicmFtcCBidWlsZGluZyBmb290cHJpbnQgZGF0YXNldCAtIG1lc29wb3RhbWlhLCBzdC4gdmluY2VudFwiLFwiTUxIVUJcIixcInJhbXAgQnVpbGRpbmcgRm9vdHByaW50IERhdGFzZXQgLSBNZXNvcG90YW1pYSwgU3QuIFZpbmNlbnRcIixcIjFcIiwyNzgxNDEyMjU2LDldIn0%3D/Western%20USA%20Live%20Fuel%20Moisture_1",
+        "href": "https://cmr.earthdata.nasa.gov/stac/MLHUB/collections?cursor=eyJqc29uIjoiW1wicmFtcCBidWlsZGluZyBmb290cHJpbnQgZGF0YXNldCAtIG1lc29wb3RhbWlhLCBzdC4gdmluY2VudFwiLFwiTUxIVUJcIixcInJhbXAgQnVpbGRpbmcgRm9vdHByaW50IERhdGFzZXQgLSBNZXNvcG90YW1pYSwgU3QuIFZpbmNlbnRcIixcIjFcIiwyNzgxNDEyMjU2LDEwXSIsInVtbSI6IltcInJhbXAgYnVpbGRpbmcgZm9vdHByaW50IGRhdGFzZXQgLSBtZXNvcG90YW1pYSwgc3QuIHZpbmNlbnRcIixcIk1MSFVCXCIsXCJyYW1wIEJ1aWxkaW5nIEZvb3RwcmludCBEYXRhc2V0IC0gTWVzb3BvdGFtaWEsIFN0LiBWaW5jZW50XCIsXCIxXCIsMjc4MTQxMjI1NiwxMF0ifQ%3D%3D/Western%20USA%20Live%20Fuel%20Moisture_1",
         "description": "This data contains manually collected live fuel moisture measurements in the western United States and remotely-sensed variables. Live fuel moisture represents the mass of water in live vegetation elements like leaves, needles, and twigs divided by its oven-dried mass. It is represented in percentages. Higher the live fuel moisture, wetter the vegetation elements, and vice versa. Live fuel moisture measurements were collected by the United States Forest Service and are available from the [National Fuel Moisture Database](https://www.wfas.net/index.php/national-fuel-moisture-database-moisture-drought-103). Each row of the data corresponds to one unique ground measurement of live fuel moisture (column named \"percent(t)\") matched with various remotely-sensed observables that may be used to predict live fuel moisture. The live fuel moisture is sampled for representative species within a 5-acre plot (or 20,000 m2) centered at the location described by the columns \"latitude\" and \"longitude\" on the day described by the column \"date\". All other columns represent remotely-sensed observables from satellites (e.g., Sentinel-1 and Landsat-8) or maps (e.g., soil texture). Temporally varying remotely-sensed observables are interpolated to 15-day periods and are provided for the date closest to the day of ground-measurement as well as for 6 fortnights preceding the day of live fuel moisture measurement. The time series of satellite data may allow for greater predictability of live fuel moisture.",
         "license": "proprietary"
     },
@@ -200403,7 +197816,7 @@
         "bbox": "-179.99, -39.06, 180, 39.01",
         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2036878925-POCLOUD.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2036878925-POCLOUD.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=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-REMSS-L3U-v7.0.1a_7.0.1a",
+        "href": "https://cmr.earthdata.nasa.gov/stac/POCLOUD/collections?cursor=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%3D%3D/WindSat-REMSS-L3U-v7.0.1a_7.0.1a",
         "description": "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO).  In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions.  By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by \"rt\" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. \"Final\" data (currently identified by \"v7.0.1a\" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final \"v7.0.1a\" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter \"a\"  refers to the file incompliance with GHRSST format.",
         "license": "proprietary"
     },
@@ -200732,32 +198145,6 @@
         "description": "Measurements taken in the western equatorial Pacific Ocean in 1996.",
         "license": "proprietary"
     },
-    {
-        "id": "a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0",
-        "title": "An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.2071304, 47.5210264, 8.2382011, 47.543743",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081770-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081770-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0",
-        "description": "Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.",
-        "license": "proprietary"
-    },
-    {
-        "id": "a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0",
-        "title": "A numerical solver for heat and mass transport in snow based on FEniCS",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8472494, 46.812044, 9.8472494, 46.812044",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0",
-        "description": "This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.     The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:    _Sch\u00fcrholt, K., Kowalski, J., L\u00f6we, H.; Elements of future snowpack modeling - Part 1: A physical  instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_    The code and potential updates can be accessed directly through git via:    https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics",
-        "license": "proprietary"
-    },
     {
         "id": "a0d9764a3068439b997c42928ef739d2_NA",
         "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series  for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2",
@@ -200979,32 +198366,6 @@
         "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31.   Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation.",
         "license": "proprietary"
     },
-    {
-        "id": "above-and-below-ground-herbivore-communities-along-elevation_1.0",
-        "title": "Above- and below-ground herbivore communities along elevation",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/above-and-below-ground-herbivore-communities-along-elevation_1.0",
-        "description": "Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0",
-        "title": "Accessibility of the Swiss forest for economic wood extraction (2021)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0",
-        "description": "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport.  A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the \"helicopter\" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based.   Road transport was investigated using network analysis, based on the data set \"Forest access roads 2013\" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account.  Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the \"helicopter\" category or transport with trucks under 26 tons).   The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland.   The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php)  Input data used:   - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)",
-        "license": "proprietary"
-    },
     {
         "id": "accum-measurements-domec-traverse-1982_1",
         "title": "Accumulation Measurements from Pioneerskaya to Dome C, 1982-84",
@@ -201538,32 +198899,6 @@
         "description": "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400  metres above sea level. The report on the third survey states \"As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall.\"  The spatial extents given in this metadata record are for the second survey.  For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and  (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level.  There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre.  Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM.",
         "license": "proprietary"
     },
-    {
-        "id": "aerosol-data-davos-wolfgang_1.0",
-        "title": "Aerosol Data Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-davos-wolfgang_1.0",
-        "description": "Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.    Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &#956;m) and GT-526S (0.3 \u2013 5 &#956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&#8321;, PM&#8322;,&#8325; and PM&#8321;&#8320; (assumptions: particle density: 1.65 g cm&#713;&#179;, refractive index: 1.5+i0).    Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &#956;m and ~20 &#956;m by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&#713;&#179;).    Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&#713;&#179;).     Coriolis &#956; and DRINCZ: A microbial air sampler (Coriolis &#956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &#956;m were drawn with an air flow rate of up to 300 l min&#713;&#185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.",
-        "license": "proprietary"
-    },
-    {
-        "id": "aerosol-data-weissfluhjoch_1.0",
-        "title": "Aerosol Data Weissfluhjoch",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.806475, 46.832964, 9.806475, 46.832964",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/aerosol-data-weissfluhjoch_1.0",
-        "description": "Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.     Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &#956;m) and GT-526S (0.3 \u2013 5 &#956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&#8321;, PM&#8322;,&#8325; and PM&#8321;&#8320; (assumptions: particle density: 1.65 g cm&#713;&#179;, refractive index: 1.5+i0).     Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &#956;m and ~20 &#956;m by the particle\u2019s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&#713;&#179;).     Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&#713;&#179;).     Coriolis &#956; and LINDA: A microbial air sampler (Coriolis &#956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &#956;m were drawn with an air flow rate of up to 300 l min&#8254;&#185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.",
-        "license": "proprietary"
-    },
     {
         "id": "aerosol_properties_725_1",
         "title": "SAFARI 2000 Physical and Chemical Properties of Aerosols, Dry Season 2000",
@@ -201616,19 +198951,6 @@
         "description": "Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.",
         "license": "proprietary"
     },
-    {
-        "id": "afforestation-stillberg_1.0",
-        "title": "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.86716, 46.773573, 9.86716, 46.773573",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081740-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081740-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/afforestation-stillberg_1.0",
-        "description": "# Background information    The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Long-term monitoring of the large-scale high-elevation afforestation has generated data about tree growth, survival, and vitality. In addition, detailed characteristics of the microsite conditions of the research were conducted. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone.     # Experiment description    The Stillberg afforestation experiment was established in 1975 by planting 92,000 seedlings of *Larix decidua*, *Pinus cembra* and *Pinus mugo* ssp. *uncinata* in the alpine treeline ecotone. The afforestation site is located on a northeast-facing slope with steep, topographically highly structured terrain and covers elevations from 2075 to 2230 m a.s.l. The afforestation site was divided into 4052 square plots of 3.5 \u00d7 3.5 m, arranged in a regular species-alternating pattern over the whole area. Each plot contained 25 trees of one species (1350 plots per species), and the seedlings were systematically planted 70 cm apart. The trees have been monitored since 1975. Specifically, tree mortality was assessed annually from 1975 until 1995 and has been documented every ten years since then, with surveys in 2005 and 2015 (the next survey is due in 2025). Height of the surviving trees was measured in 1975, 1979, 1982, 1985, 1990, 1995, 2005, and 2015. In 1995, 2005, and 2015, drivers of tree vitality were assessed for a subset of trees per plot. Additionally, an extensive set of environmental parameters characterizing microsite conditions of the afforestation area were recorded before and after the planting of the trees.     # Data description    The five datasets from the afforestation experiment comprise ecological and environmental data from the main afforestation experiment in five datasets with accompanying metadata (Stillberg_afforestation_all_metadata.xlsx). All data and metadata files are bundled in a ZIP-file (Stillberg_afforestation_v1.zip). In particular, a first dataset contains environmental data characterising microsite conditions of the 4000 plots with regard to soil, topography, vegetation and microclimatic conditions (Stillberg_afforestation_plot_data_v1.csv; Stillberg_afforestation_plot_metadata_v1.csv. In each plot, the natural tree regeneration was assessed by counting seedings of several tree species in 2005 and 2015 (Stillberg_afforestation_regeneration_data_v1.csv; Stillberg_afforestation_regeneration_metadata_v1.csv). Furthermore, specific information about each of the 92\u2019000 planted trees of the tree species is available (Stillberg_afforestation_tree_parameter_data_v1.csv; Stillberg_afforestation_tree_parameter_metadata_v1.csv). Survival data for each of the 92\u2019000 individual trees can be found in a separate dataset (Stillberg_afforestation_tree_survival_data_v1.csv; Stillberg_afforestation_tree_survival_metadata_v1.csv). Tree growth and vitality parameters are available for all trees from 1995, and for subsets of trees for 2005 and 2015 (Stillberg_afforestation_tree_measurements_data_v1.csv; Stillberg_afforestation_tree_measurements_metadata_v1.csv).",
-        "license": "proprietary"
-    },
     {
         "id": "afm06ihd_240_1",
         "title": "BOREAS AFM-06 Boundary Layer Height Data",
@@ -201798,19 +199120,6 @@
         "description": "This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005.  The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf).",
         "license": "proprietary"
     },
-    {
-        "id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0",
-        "title": "Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0",
-        "description": "Supplementary material for the publication \" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis\" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G.    Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions.",
-        "license": "proprietary"
-    },
     {
         "id": "air_methane_lawdome_1",
         "title": "Dated Readings For Air Composition And Methane From Law Dome Ice Core",
@@ -201993,19 +199302,6 @@
         "description": "Acoustic Doppler Current Profiler (ADCP) observations, were             collected from the R/V Seward Johnson on two cruises to the Georges             Bank region, April-June 1995.  Three different ADCP units were used:             two broadband at 150 and 600 kHz, and one narrowband at 150 kHz.  The             broadband 150 kHz unit was used at anchor stations with data reported             at hourly intervals.  The broad-band 600 kHz and narrow-band 150 kHz             units collected data in the along track mode with data reported at             five minute intervals.  For each time interval, the u and v components             of currents are reported at uniform depth intervals throughout the             water column.               Ship                cruise        dates  R/V Seward Johnson  9506   1995 04 25 1995 05 02  R/V Seward Johnson  9508   1995 06 06 1995 06 16",
         "license": "proprietary"
     },
-    {
-        "id": "alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0",
-        "title": "Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the \u201cFlavescence dor\u00e9e\u201d epidemics",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4484863, 45.8115721, 9.4372559, 46.4586735",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0",
-        "description": "Flavescence dor\u00e9e (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported.     Data used for the publication.  Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses.  Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dor\u00e9e phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees.",
-        "license": "proprietary"
-    },
     {
         "id": "alos-prism-l1c_NA",
         "title": "ALOS PRISM L1C",
@@ -202019,32 +199315,6 @@
         "description": "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East.  The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: \u2022\tTime window: from 2006-08-01 to 2011-03-31 \u2022\tOrbits: from 2768 to 27604 \u2022\tPath (corresponds to JAXA track number): from 1 to 665 \u2022\tRow (corresponds to JAXA scene centre frame number): from 310 to 6790.  The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors).   The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width.  The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention.",
         "license": "proprietary"
     },
-    {
-        "id": "alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0",
-        "title": "Alpine3D simulations of future climate scenarios for Graubunden",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.6737061, 46.2216525, 10.6347656, 47.1075228",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0",
-        "description": "This is the simulation dataset from _\"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland\"_, M. Bavay, T. Gr\u00fcnewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013    A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graub\u00fcnden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.    The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.    Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.",
-        "license": "proprietary"
-    },
-    {
-        "id": "als-based-snow-depth_1.0",
-        "title": "ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8683834, 46.829474, 9.8683834, 46.829474",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814552-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814552-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/als-based-snow-depth_1.0",
-        "description": "This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA\u2019s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.   All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.     __Paper Citation:__   > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198\u2013 6216,  [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._",
-        "license": "proprietary"
-    },
     {
         "id": "amanda_bay_sat_1",
         "title": "Amanda Bay Satellite Image Map 1:100 000",
@@ -202084,45 +199354,6 @@
         "description": "The NASA Ames Airborne Tracking 14-channel Sunphotometer (AATS-14) was operated successfully aboard the University of Washington CV-580 for 24 data flights during the dry-season airborne campaign from August 13 to September 25, 2000. Flights originated from Pietersburg, South Africa; Kasane, Botswana; and Walvis Bay, Namibia. The AATS-14 instrument measures the transmission of the direct solar beam at 14 discrete wavelengths (350-1558 nm) from which we derived spectral aerosol optical depths (AOD) and columnar water vapor (CWV).",
         "license": "proprietary"
     },
-    {
-        "id": "amount_of_dead_wood-214_1.0",
-        "title": "Amount of dead wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814565-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814565-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/amount_of_dead_wood-214_1.0",
-        "description": "Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs  starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "amphibian-and-landscape-data-swiss-lowlands_1.0",
-        "title": "Amphibian and urban-rural landscape data Swiss Lowlands",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.7124023, 47.0776041, 9.0637207, 47.7983967",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814582-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814582-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/amphibian-and-landscape-data-swiss-lowlands_1.0",
-        "description": "The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Z\u00fcrich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban \"green\" and \"grey\" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.",
-        "license": "proprietary"
-    },
-    {
-        "id": "amphibian-data-aargau_1.0",
-        "title": "Amphibian observation and pond data (Aargau, Switzerland)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "7.7, 47.15, 8.46, 47.62",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814599-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814599-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/amphibian-data-aargau_1.0",
-        "description": "In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width \u22656m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each.  The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.  The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.",
-        "license": "proprietary"
-    },
     {
         "id": "amprimpacts_1",
         "title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS",
@@ -202357,32 +199588,6 @@
         "description": "AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster.",
         "license": "proprietary"
     },
-    {
-        "id": "anezet-analysing-net-zero-transformations_1.0",
-        "title": "ANEZET: Analysing Net-Zero Transformations",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/anezet-analysing-net-zero-transformations_1.0",
-        "description": "We have analysed past transformations in Switzerland in four environmental domains, with the aim to  draw conclusions for current challenges, such as the net\u2010zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research.",
-        "license": "proprietary"
-    },
-    {
-        "id": "angle-of-repose-of-snow_1.0",
-        "title": "Angle of repose experiments with natural and spherical snow",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814625-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814625-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/angle-of-repose-of-snow_1.0",
-        "description": "Angle of repose experiments were performed with different snow types at temperatures between -2 and -40\u00b0C. They were used to  examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.     This dataset consists of  1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.    The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.",
-        "license": "proprietary"
-    },
     {
         "id": "ant_dist_1",
         "title": "Antarctic Distances",
@@ -202448,19 +199653,6 @@
         "description": "Antarctic Single Frame Records are a collection of aerial photographs over Antarctica from the United States Antarctic Resource Center (USARC) and the British Antarctic Survey (BAS) dating from 1946 to 2000. The Antarctic Single Frame Records collection includes black-and-white, natural color and color infrared images with a photographic scale ranging from 1:1,000 to 1:64,000. ",
         "license": "proprietary"
     },
-    {
-        "id": "anthropogenic-change-and-net-n-mineralization_1.0",
-        "title": "Anthropogenic change and soil net N mineralization",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "158.90625, -54.9776137, -132.1875, 61.2702328",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814650-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814650-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/anthropogenic-change-and-net-n-mineralization_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Sch\u00fctz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology    Please cite this paper together with the citation for the datafile.    We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth\u2019s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).",
-        "license": "proprietary"
-    },
     {
         "id": "aoci0bil_281_1",
         "title": "BOREAS Level-0 AOCI Imagery: Digital Counts in BIL Format",
@@ -202526,32 +199718,6 @@
         "description": "The Autonomous Parsivel Unit (APU) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format from January 15, 2020 through February 29, 2020.",
         "license": "proprietary"
     },
-    {
-        "id": "area_of_shrub_forest-123_1.0",
-        "title": "Area of shrub forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814712-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814712-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/area_of_shrub_forest-123_1.0",
-        "description": "All plots classified as shrub forest according to the NFI forest definition.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "arthropod-biomass-abundance-species-richness-trends-limpach_1.0",
-        "title": "Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "7.3819542, 47.0815787, 7.528553, 47.1334543",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814758-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814758-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/arthropod-biomass-abundance-species-richness-trends-limpach_1.0",
-        "description": "Recent publications about declines in arthropod biomass, abundance and species diversity  raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types  were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58\u2019255 specimens of 1\u2019343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics.    Data are provided on request to contact person against bilateral agreement.",
-        "license": "proprietary"
-    },
     {
         "id": "asas_Not provided",
         "title": "Advanced Solid-state Array Spectroradiometer (ASAS)",
@@ -202630,45 +199796,6 @@
         "description": "This record describes GIS polygon data (a shapefile) representing the boundaries of Antarctic Specially Protected Areas (ASPAs) and an Antarctic Specially Managed Area (ASMA) in the Australian Antarctic Territory for which Australia was the proponent or co-proponent. Also included is the boundary of ASPA 168 for which China was the proponent.  The following is a list of the ASPAs and ASMA: ASPA 101 Taylor Rookery ASPA 102 Rookery Islands ASPA 103 Ardery Island and Odbert Island ASPA 135 North-east Bailey Peninsula ASPA 136 Clark Peninsula ASPA 143 Marine Plain ASPA 160 Frazier Islands ASPA 162 Mawson's Huts ASPA 164 Scullin and Murray Monoliths ASPA 167 Hawker Island ASPA 168 Mt Harding ASPA 169 Amanda Bay ASPA 174 Stornes ASMA 6 Larsemann Hills  The data is available from a link in this metadata record and also, as a separate shapefile for each ASPA or ASMA, from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database (see related url). GIS data representing the boundaries of other ASPAs and ASMAs is also available from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database.",
         "license": "proprietary"
     },
-    {
-        "id": "asrb-dav_1.0",
-        "title": "ASRB_DAV: Shortwave and longwave radiation measurements (2 min) in Davos Dorf",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.84827, 46.81277, 9.84827, 46.81277",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814851-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814851-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/asrb-dav_1.0",
-        "description": "Incoming and outgoing  shortwave and longwave 2 min radiation measurements in Davos Dorf, CH.     ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.",
-        "license": "proprietary"
-    },
-    {
-        "id": "asrb-vf_1.0",
-        "title": "ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.809204, 46.829631, 9.809204, 46.829631",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814947-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814947-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/asrb-vf_1.0",
-        "description": "Incoming and outgoing  shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153\u2013366 cm over the period 1934\u20132012. The measurement site is located in an almost flat part of a southeast oriented slope.    ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.",
-        "license": "proprietary"
-    },
-    {
-        "id": "asrb-wfj_1.0",
-        "title": "ASRB_WFJ: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.809204, 46.829631, 9.809204, 46.829631",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814987-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814987-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/asrb-wfj_1.0",
-        "description": "Corrected incoming and outgoing  shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH.     ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.",
-        "license": "proprietary"
-    },
     {
         "id": "aster_1",
         "title": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre",
@@ -202734,19 +199861,6 @@
         "description": "Dave Gardner was on Heard Island in January and February 2000 as part of the 2000 ANARE.  Opportunistic use was made of the the differential gps system to take accurate locations of 16 points identified from the 1987 aerial photography, so that they could be used as reference points for merging the photographs into an accurate photo mosaic.  Around the station and to the NE it was easy to identify features from the photographs with confidence.  To the west of the station the topography and features of the azorella wallows had changed significant and it was not possible to identify features with confidence.",
         "license": "proprietary"
     },
-    {
-        "id": "atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0",
-        "title": "ATLFISHREF A 12S mitochondrial reference dataset for metabarcoding Atlantic Fishes frequently caught during scientific surveys in the Bay of Biscay",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2024-01-01",
-        "end_date": "2024-01-01",
-        "bbox": "-5.6469727, 43.0158943, -0.9008789, 48.3810645",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081750-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081750-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0",
-        "description": "The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade.  Thanks to a large spatio-temporal coverage,  high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the \u201cTeleo\u201d barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature).  This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.",
-        "license": "proprietary"
-    },
     {
         "id": "atmos_co2_by_erosion_xdeg_1019_1",
         "title": "ISLSCP II Atmospheric Carbon Dioxide Consumption by Continental Erosion",
@@ -202760,58 +199874,6 @@
         "description": "The Continental Atmospheric CO2 Consumption data set represents gridded estimates for the riverine export of carbon and of sediments based on empirical models. All data exist for the overall continental area in a spatial resolution of 0.5 x 0.5 degree longitude/ latitude. The units are tC/km2/yr for all carbon species, and t/km2/yr for sediment fluxes. There are two data files (*.zip) with this data set which describe the following: dissolved organic carbon (DOC) export, particulate organic carbon (POC) export, bicarbonate export, export of bicarbonate being of atmospheric origin (also called atmospheric CO2 consumption by rock weathering), and sediment export.",
         "license": "proprietary"
     },
-    {
-        "id": "atree-forest-owner-clearances-offsetting_1.0",
-        "title": "ATREE forest owners survey about forest clearances offsetting in the forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.0175171, 46.7689338, 7.800293, 47.1027556",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815028-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815028-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/atree-forest-owner-clearances-offsetting_1.0",
-        "description": "In April 2020, about 1700 forest owners of the plateau region of the Canton of Berne were invited to participate in a survey (virtually all of them received a conventional paper-pencil questionnaire) about their willingness to provide forest nature conservation measures in their forest to compensate forest clearances that cannot be compensated by afforestation. The questionnaire contained a survey experiment (conjoint analysis) that offered a choice between two options and the status quo in 9 decision-making situations. Of the 607 completed questionnaires that were returned the survey experiment was completed by about 400.",
-        "license": "proprietary"
-    },
-    {
-        "id": "atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0",
-        "title": "ATREE forest owners survey about climate regulation services of forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814546-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814546-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0",
-        "description": "Forest owners of the Canton of Lucerne were survey about their willingness to employ different forest management measures to provicde climate regulation services by forests. Of the nearly 3000 forest owners that received an invitation to a online-survey and the 900 forest owners that received a paper and pencil survey, 1055 valid responses were received. The questionnaire contained a survey experiment in which 9 choice situations were presented to the respondents in which they had the choice between two options and the status quo. This survey experiment part of the survey was completed by 990 respondents.",
-        "license": "proprietary"
-    },
-    {
-        "id": "atree-q-methodology-forest-clearances-offsetting_1.0",
-        "title": "ATREE Q-methodology statement sorts on forest clearances offsetting in the forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814556-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814556-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/atree-q-methodology-forest-clearances-offsetting_1.0",
-        "description": "In Novdember 2019 about 19 experts on forest surface protection and forest clearances were invited to a workshop in order to discuss policy design and implementation problems regarding the offsetting of forest clearances. In Switzerland such offsetting can be provided under certain circumstances by implementing forest nature conservation measures in the forest instead of providing in-kind compensation, i.e. reafforestation on agricultural land. The workshop included the sorting of 34 statements \u2013 that were elaborated beforehand, partially also with help of the participants \u2013 according to the \"Q-methodology\" survey technique (participants arrange given statements about a certain subject into boxes that are normally distributed over a \"agree - do not agree\" answer scale). The participants included representatives from cantonal and national forest administrations, nature conservation NGOs, forest NGOs, spatial planning NGOs, private counseling enterprises as well as national, cantonal and regional forest owner organizations. The data allows a factor analytical differentiation of actors into groups with distinct positions towards forest clearance compensation as well as a positioning of these groups relative to each statement.",
-        "license": "proprietary"
-    },
-    {
-        "id": "atree-social-network-analysis-carbon-sequestration-lucerne_1.0",
-        "title": "ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.0859375, 46.9348859, 8.470459, 47.2191951",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814569-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814569-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/atree-social-network-analysis-carbon-sequestration-lucerne_1.0",
-        "description": "In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the \"actor coalition framework\" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations.",
-        "license": "proprietary"
-    },
     {
         "id": "atrs_Not provided",
         "title": "Airborne Coherant Radar Sounding Data",
@@ -203176,97 +200238,6 @@
         "description": "The M7 data represent the highest points in each 30 minute by 30       minute grid square for Australia.              see: 'http://www.ga.gov.au/'              The following text was abstracted from Bruce Gittings' Digital       Elevation Data Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'.       The catalogue is a comprehensive source of information on digital       elevation data and should be retrieved in its entirety for additional       information.              Australian Data is available from the Australian Survey and Land       Information Group (AUSLIG).  There are three products; M7 are Critical       Aeronautical Heights which represent the highest point in each 30'x30'       quad, M8 are Spot heights (ie. an irregular grid) and M9 represents an       18\" (~500m) grid at 1:250,000 scale (gridded from M8 using an       Hutchinson Algorithm).  Both M8 and M9 have incomplete coverage of the       country.              The 500m grid covers 30% of Australia (Southern New South Wales,       Victoria, parts of Northern Queensland and selected cities).  The size       of the 1:250 000 scale files is 60,501 points each x 63 files =       38,176,131 elevation points.  Costs: License 1:250 000 AU$1000 / File       1:100 000 AU$250.              NB. 1996: 100m and 200m DEMs covering all of Australia are also       now available.  Prices are as follows (in US Dollars):              Per km2      Total Cost  Copyright Restrictions       -------      ----------  ----------------------       100m DEM    $0.0028      $21,433     One-time license fee       200m DEM    $0.0017      $13,089     \"  \"              URL: 'http://www.auslig.gov.au/'",
         "license": "proprietary"
     },
-    {
-        "id": "automated-avalanche-release-area-pra-delineation-davos_1.0",
-        "title": "Automated Avalanche Release Area (PRA) Delineation Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.7503662, 46.7125608, 9.8876953, 46.8517391",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814581-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814581-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/automated-avalanche-release-area-pra-delineation-davos_1.0",
-        "description": "This dataset contains the output and reference data published in the paper \"Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping\" Yves B\u00fchler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.    Abstract:  Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.    In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 \u2013 30 years return period) and extreme (100 \u2013 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.",
-        "license": "proprietary"
-    },
-    {
-        "id": "automatic-classification-of-avalanches_1.0",
-        "title": "Automatic Classification of Avalanches",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.9223037, 46.7200638, 9.9223037, 46.7200638",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814596-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814596-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/automatic-classification-of-avalanches_1.0",
-        "description": "This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "avalanche-accidents-in-switzerland-since-1970-71_1.0",
-        "title": "Avalanche accidents in Switzerland since 1970/71",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081213-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081213-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/avalanche-accidents-in-switzerland-since-1970-71_1.0",
-        "description": "**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.    This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one person involved (caught). The data set commences on 01/10/1970. After the completion of a hydrological year, the new data is added.    The following information is provided:     * avalanche identifier      * date of the accident     * accuracy of the date in range of days before and after    * canton     * municipality     * start zone point latitude   * start zone point longitude   * start zone point accuracy (in meters)   * start zone point elevation (in meteres above sea level)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)       * forecasted avalanche danger level 1 (first danger)     * forecasted avalanche danger level 2 (second danger)   * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)    * number of dead persons      * number of caught persons     * number of fully buried persons     * activity/location of the accident party at the time of the incident",
-        "license": "proprietary"
-    },
-    {
-        "id": "avalanche-fatalities-european-alps-1969-2015_1.0",
-        "title": "Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "4.5703125, 43.0367759, 16.5673828, 48.4292006",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814622-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814622-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/avalanche-fatalities-european-alps-1969-2015_1.0",
-        "description": "During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.    Data complementing Figure 2 in:    _\"Avalanche fatalities in the European Alps: long-term trends and statistics\"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pav\u0161ek, M., Valt, M., and Darms, G.    Data description:    please refer to section 2 (Data and Methods) in the mentioned publication",
-        "license": "proprietary"
-    },
-    {
-        "id": "avalanche-fatalities-per-calendar-year-since-1936_1.0",
-        "title": "Number of avalanche fatalities per calendar year in Switzerland since 1937",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814645-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814645-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/avalanche-fatalities-per-calendar-year-since-1936_1.0",
-        "description": "Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which  is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset. If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]  The following information is contained (by column and column title):    - year    - number of fatalities in the backcountry (=tour)    - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)    - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)    - number of fatalities in or around buildings or in settlements (= buildings)    - sum (of all four categories)  The definitions for these four categories, as described in the guidelines to the avalanche accident database are:  __tour:__   activities include back-country ski, snowboard or snow-shoe touring    __offpiste:__   access from ski area, generally from the top of a skilift with short hiking distances    __transportation.corridors__ (Techel et al., 2016):   people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)    __buildings__ (Techel et al., 2016):   people inside or just outside buildings, and workers on high alpine building sites",
-        "license": "proprietary"
-    },
-    {
-        "id": "avalanche-fatalities-switzerland-1936_1.0",
-        "title": "Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814658-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814658-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/avalanche-fatalities-switzerland-1936_1.0",
-        "description": "Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per  hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.   The following information is contained (by column and column title):    - hydrological year    - number of fatalities in the backcountry (=tour)    - number of fatalities in terrain close to ski areas (=offpiste)    - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)    - number of fatalities in or around buildings or in settlements (= buildings)    - sum (of all four categories)    The definition for these four categories as described in the guidelines to the avalanche accident database:         **tour**:      activities include back-country ski, snowboard or snow-shoe touring    **offpiste**:   access from ski area, generally from the top of a skilift with short hiking distances    **transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):    people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)     **buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):   people inside or just outside buildings, and workers on high alpine building sites",
-        "license": "proprietary"
-    },
-    {
-        "id": "avalanche-prediction-snowpack-simulations_1.0",
-        "title": "Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081494-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081494-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/avalanche-prediction-snowpack-simulations_1.0",
-        "description": "The data set contained in this repository was used in the analysis by Mayer et al. (2023):  Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.",
-        "license": "proprietary"
-    },
     {
         "id": "avapsimpacts_1",
         "title": "Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS",
@@ -203501,19 +200472,6 @@
         "description": "The \"Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)\" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between \u201339.5\u00b0C and +87\u00b0C with a radiometric resolution of 0.5\u00b0C. A value of \u201340\u00b0C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/",
         "license": "proprietary"
     },
-    {
-        "id": "bark-and-wood-boring-insects-in-pines_1.0",
-        "title": "Infestation of Scots pines with different vitalities by bark and wood boring insects",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.5627136, 46.2249145, 7.8984833, 46.3184179",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814729-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814729-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-and-wood-boring-insects-in-pines_1.0",
-        "description": "After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack.",
-        "license": "proprietary"
-    },
     {
         "id": "baro-levelling-to-domec_1",
         "title": "Barometric Leveling Results, Pioneerskaya to Dome C",
@@ -203540,58 +200498,6 @@
         "description": "Measurements taken of barometric pressure and air temperature during traverse across Law Dome and Wilkes Land in 1968.  Copies of these documents have been archived in the records store of the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "basal_area-92_1.0",
-        "title": "Basal area",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814819-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814819-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/basal_area-92_1.0",
-        "description": "Sum of the stem cross-section areas of all living trees and shrubs  starting at 12 cm dbh (standing and lying) at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "basal_area_of_dead_wood-171_1.0",
-        "title": "Basal area of dead wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814926-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814926-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/basal_area_of_dead_wood-171_1.0",
-        "description": "Sum of the stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "basal_area_of_dead_wood_nfi1-247_1.0",
-        "title": "Basal area of dead wood NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814964-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814964-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/basal_area_of_dead_wood_nfi1-247_1.0",
-        "description": "Sum of stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height) recorded according to the NFI1 method.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "base-cation-dynamics-in-an-oriental-beech-forest_1.0",
-        "title": "Base cation dynamics in an Oriental beech forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "54.216156, 36.4371199, 54.2518616, 36.4570042",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815006-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815006-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/base-cation-dynamics-in-an-oriental-beech-forest_1.0",
-        "description": "Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.  In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.",
-        "license": "proprietary"
-    },
     {
         "id": "basin_border_670_1",
         "title": "LBA Regional Boundary for the Amazon and Tocantins River Basins, 5-min",
@@ -203618,19 +200524,6 @@
         "description": "The Australian Antarctic Division (AAD) has developed a proposal for the establishment of seven Marine Protected Areas (MPAs) located around east Antarctica for the purposes of marine ecosystem conservation. As seafloor morphology is a key component of marine ecosystems, this bathymetry compilation for the proposed MPAs was produced to support the AAD proposal. All bathymetry data available to Geoscience Australia at the time of compilation were used. This included multibeam and singlebeam acoustic data which were verified and processed to ensure the data were as accurate as possible. Processing included sound velocity corrections, navigation verification and the rejection of erroneous data points. Once processed, the data were gridded to 100m resolution and projected into suitable WGS84 UTM zones. The gridded data was exported into several formats to facilitate ease of use. The formats include xyz files, ESRI rasters, geoTIFs, CARISTM image files and soundings.  The data and the technical report are available for download from URLs below.",
         "license": "proprietary"
     },
-    {
-        "id": "bats-and-nocturnal-insects-in-urban-green-areas_1.0",
-        "title": "Bats and nocturnal insects in urban green areas",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "1.8237305, 47.2195681, 8.8110352, 51.5360856",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814542-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814542-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/bats-and-nocturnal-insects-in-urban-green-areas_1.0",
-        "description": "Animal biodiversity in cities is generally expected to be uniformly reduced, but recent studies show that this is modulated by the composition and configuration of Urban Green Areas (UGAs). UGAs represent a heterogeneous network of vegetated spaces in urban settings that have repeatedly shown to support a significant part of native diurnal animal biodiversity. However, nocturnal taxa have so far been understudied, constraining our understanding of the role of UGAs on maintaining ecological connectivity and enhancing overall biodiversity. We present a well-replicated multi-city study on the factors driving bat and nocturnal insect biodiversity in three European cities. To achieve this, we sampled bats with ultrasound recorders and flying insects with light traps during the summer of 2018. Results showed a greater abundance and diversity of bats and nocturnal insects in the city of Zurich, followed by Antwerp and Paris. We identified artificial lighting in the UGA to lower bat diversity by probably filtering out light-sensitive species. We also found a negative correlation between both bat activity and diversity and insect abundance, suggesting a top-down control. An in-depth analysis of the Zurich data revealed divergent responses of the nocturnal fauna to landscape variables, while pointing out a bottom-up control of insect diversity on bats. Thus, to effectively preserve biodiversity in urban environments, UGAs management decisions should take into account the combined ecological needs of bats and nocturnal insects and consider the specific spatial topology of UGAs in each city.",
-        "license": "proprietary"
-    },
     {
         "id": "bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA",
         "title": "TerraSAR-X - Staring Spotlight Images (TerraSAR-X Staring Spotlight)",
@@ -203683,32 +200576,6 @@
         "description": "This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour.  The dataset contains three files - an image file and two zip files.  The image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations.  The zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island.  The zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations.",
         "license": "proprietary"
     },
-    {
-        "id": "beech_stress_thresholds_1.0",
-        "title": "Stress thresholds of mature European beech trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "6.5368652, 45.9799133, 9.7009277, 47.6044342",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814551-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814551-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/beech_stress_thresholds_1.0",
-        "description": "This data set contains the data presented in the figures 1-6 in Walthert et al. (2020): From the comfort zone to crown dieback: sequence of physiological stress thresholds in mature European beech trees across progressive drought. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2020.141792.  A detailed methodical description of the data can be found in the Material and Methods section of the paper.    Drought responses of mature trees are still poorly understood making it difficult to predict species distributions under a warmer climate. Using mature European beech (Fagus sylvatica L.), a widespread and economically important tree species in Europe, we aimed at developing an empirical stress-level scheme to describe its physiological response to drought. We analysed effects of decreasing soil and leaf water potential on soil water uptake, stem radius, native embolism, early defoliation and crown dieback with comprehensive measurements from overall nine hydrologically distinct beech stands across Switzerland, including records from the exceptional 2018 drought and the 2019/2020 post-drought period. Based on the observed responses to decreasing water potential we derived the following five stress levels: I (predawn leaf water potential >-0.4 MPa): no detectable hydraulic limitations; II (-0.4 to -1.3): persistent stem shrinkage begins and growth ceases; III (-1.3 to -2.1): onset of native embolism and defoliation; IV (-2.1 to -2.8): onset of crown dieback; V (<-2.8): transpiration ceases and crown dieback is >20%. Our scheme provides, for the first time, quantitative thresholds regarding the physiological downregulation of mature European beech trees under drought and therefore synthesises relevant and fundamental information for process-based species distribution models. Moreover, our study revealed that European beech is drought vulnerable, because it still transpires considerably at high levels of embolism and because defoliation occurs rather as a result of embolism than preventing embolism. During the 2018 drought, an exposure to the stress levels III-V of only one month was long enough to trigger substantial crown dieback in beech trees on shallow soils. On deep soils with a high water holding capacity, in contrast, water reserves in deep soil layers prevented drought stress in beech trees. This emphasises the importance to include local data on soil water availability when predicting the future distribution of European beech.",
-        "license": "proprietary"
-    },
-    {
-        "id": "bender2020_1.0",
-        "title": "Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.7568359, 45.7828484, 10.7336426, 48",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814563-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814563-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/bender2020_1.0",
-        "description": "This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.    This includes:     * meteorological forcing,   * climate change timeries and   * simulation files together with   * snow depth  * ground temperature     __Please refer to the following publication for further details which should be cited when using this dataset:__    __Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.__",
-        "license": "proprietary"
-    },
     {
         "id": "beryllium_10be_isotopes_lawdome_1",
         "title": "High resolution studies of cosmogenic beryllium isotopes (10Be) at Law Dome",
@@ -203735,19 +200602,6 @@
         "description": "Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output.  A ~1.4 x 1 x 1 m pit was dug on Law Dome. The wall was flattened using a ~60 cm level, handsaw and paint scrappers. A significant sastrugi could be seen in the top right of the wall. Sampling was started on the left of the wall to avoid this where possible. Wearing plastic gloves to avoid contaminating the samples, the top surface was levelled to the lowest point, and some of the snow collected as sample P1-1. It was around 4 cm at its highest point. A 10 cm x 10 cm grid was drawn into the wall, covering 80 cm x 80 cm. The top 10 cm layer was sawn out of the wall using a hand saw, cutting into the wall by at least 20 cm along the horizontal 10 cm below the top surface, then the back 20 cm from the front surface, and finally chopping the large block into smaller blocks.  The extra six blocks were discarded, and the two samples were put into zip lock bags as P2-1 and P2-2. The back of the sampling area was cleared back to allow easier access for the next layer. This was repeated for seven more layers, finishing with P9.  One block from each level was used for density measurements. The samples from each level were combined into a melting jar and carrier added. For some samples, not all the blocks fitted at once, so a portion of the blocks were melted (with the carrier) in the oven at 60 degrees C. The samples were allowed melt completely overnight. ~10mL of the samples were retained for water isotopes . The samples were filtered though 41 microns and the 0.45 microns and pumped onto cation columns.",
         "license": "proprietary"
     },
-    {
-        "id": "bet_1.0",
-        "title": "Bryophytes of Europe Traits (BET) dataset",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "-31.1718714, 26.214591, 70.1953197, 82.3206462",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081833-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081833-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/bet_1.0",
-        "description": "The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).",
-        "license": "proprietary"
-    },
     {
         "id": "bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA",
         "title": "MERIS - Water Parameters - Baltic Sea, Monthly",
@@ -203878,32 +200732,6 @@
         "description": "The Southern African Regional Science Initiative (SAFARI 2000) was conducted in part to investigate the impacts of the large-scale transport and deposition of increasingly anthropogenic emissions on southern African biogeochemical cycling. Aerosol samples from the Mongu site in eastern Zambia were collected and analyzed to identify chemical biomarkers during the SAFARI 2000 dry season field campaign. Total suspended particulate aerosol samples were collected diurnally for a period of two weeks during August and September of 2000.These data include bulk organic carbon, nitrogen and sulfur stable isotopic measurements of total suspended particulate aerosols and gas chromatography/mass spectrometry (GC/MS) analysis of fatty acids extracted from collected aerosols. These data were used to chemically describe temporal variability in aerosol compositions.",
         "license": "proprietary"
     },
-    {
-        "id": "bioclim_plus_1.0",
-        "title": "CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814586-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814586-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/bioclim_plus_1.0",
-        "description": "A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world\u2019s ecosystems and associated services to societies.",
-        "license": "proprietary"
-    },
-    {
-        "id": "biodiversity-integration_1.0",
-        "title": "Replication files for \"Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics\"",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814605-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814605-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/biodiversity-integration_1.0",
-        "description": "## Introduction    The ZIP file contains all data and code to replicate the analyses reported in the following paper.    Reber, U., Fischer, M., Ingold, K., Kienast, F., Hersperger, A. M., Gr\u00fctter, R., & Benz, R. (2022). Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics. *Policy Sciences*. [https://doi.org/10.1007/s11077-022-09456-4](https://doi.org/10.1007/s11077-022-09456-4)    If you use any of the material included in this repository, please refer to the paper. If you use (parts of) the text corpus, please also refer to the sources used for its compilation listed below. The content of the texts may not be changed.    ## Data folder    The data folder contains the following files.    * _corpus.parquet_: Text corpus of Swiss policy documents  * _dict_de.csv_: Biodiversity dictionary (German)  * _dict_fr.csv_: Biodiversity dictionary (French)  * _dict_it.csv_: Biodiversity dictionary (Italian)  * _topic_labels.csv_: labels/codes for policy sectors  * _topics.csv_: labels/codes for policy sectors    The corpus and the dictionary were compiled by the authors specifically for this project. The labels/codes for policy sectors are based on the [coding scheme](http://ws-old.parlament.ch/affairs/topics) of the Swiss Parliament.      ### Text corpus    The text corpus consists of 439,984 Swiss policy documents in German, French, and Italian from 1999 to 2018. The corpus was compiled from the following source between 2020-10-01 and 2021-01-31.    * Transcripts and parliamentary businesses (e.g. questions, motions, parliamentary initiatives) via the [Web Services (WS)](https://www.parlament.ch/de/%C3%BCber-das-parlament/fakten-und-zahlen/open-data-web-services) provided by the Swiss Parliament  * The official compilation of federal legislation (\"Amtliche Sammlung\", AS) via [opendata.swiss](https://opendata.swiss/de/dataset/official-compilation-of-federal-legislation-bs-as-1947-2018) provided by the Swiss Federal Archives (SFA)  *  The federal gazette (\"Bundesblatt\") via [fedlex.admin.ch](https://www.fedlex.admin.ch/de/fga/index)  * Decisions of federal courts via [entscheidsuche.ch (ES)](https://entscheidsuche.ch/)    The corpus is stored in a single data frame to use with R saved as [PARQUET](https://parquet.apache.org/) file (corpus.parquet). The data frame has the following structure.    * _text_id_: Unique identifier for each text (source information as prefix, e.g. \"t_\")  * _doc_type_: Document type (see coding scheme below)  * _branch_: Government branche (1 legislative, 2 executive, 3 judicative)  * _stage_: Stage of policy process (1 drafting, 2 introduction, 3 interpretation)  * _year_: Year of publication  * _topic_: Policy sector (coding scheme in separate file in data folder)  * _lang_: Language (de, fr, it)  * _text_: Text    The following list contains the coding scheme for the doc_type variable.    * 101: Federal gazette // Draft for public consultation (\"Vernehmlassungsverfahren\")  * 102: Federal gazette // Explanation of draft for parliament (\"Botschaft\")  * 103: Federal gazette // Strategy, action plan  * 104: Federal gazette // Federal council decree (\"Bundesratsbeschluss\")  * 105: Federal gazette // (Simple) Federal decree (\"(Einfacher) Bundesbeschluss\")  * 106: Federal gazette // General decree (\"Allgemeinverf\u00fcgung\")  * 107: Federal gazette // Treaty (\"\u00dcbereinkommen\")  * 108: Federal gazette // Treaty (\"Abkommen\")  * 109: Federal gazette // Draft for parliament (\"Entwurf\")  * 110: Federal gazette // Report (\"Bericht\")  * 111: Federal gazette // Report of parliamentary comission (\"Bericht\")  * 112: Federal gazette // Report of federal council (\"Bericht\")  * 201: Parl. businesses // Submitted text  * 202: Parl. businesses // Reason text  * 203: Parl. businesses // Federal council response  * 204: Parl. businesses // Initial situation  * 205: Parl. businesses // Proceedings  * 301: Parl. transcripts // Speech of MP  * 302: Parl. transcripts // Speech of federal council  * 401: Federal legislation // Legal text of the official compilation (law, ordinances, etc.)  * 501: Court decisions // Federal Supreme Court  * 502: Court decisions // Federal Criminal Court  * 503: Court decisions // Federal Administrative Court    ## Code folder    The code folder contains all R code for the analyses. The files are numbered chronologically.     * _1_classifier_training.R_: Training of classifiers for classification of policy sectors  * _2_classifier_application.R_: Classification of documents in corpus  * _3_dictionary_application.R_: Biodiversity indexing of documents in corpus  * _4_stm_truncation.R_: Truncation of indexed documents to keep only relevant parts  * _5_stm_translation.R_: Translation of FR and IT documents to DE  * _6_stm_model.R_: Preprocesssing and structural topic model  * _7_plots.R_: Plots and numbers as included in the paper    The code/functions folder contains custom functions used in the scripts, e.g. to support topic model interpretation.    Package versions and setup details are noted in the code files.    ## Contact    Please direct any questions to Ueli Reber (ueli.reber@eawag.ch).",
-        "license": "proprietary"
-    },
     {
         "id": "biofuel_emissions_753_1",
         "title": "SAFARI 2000 Gas Emissions from Biofuel Use and Production, September 2000",
@@ -203917,45 +200745,6 @@
         "description": "Domestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively. As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.",
         "license": "proprietary"
     },
-    {
-        "id": "biogas-aus-hofdunger-in-der-schweiz_1.0",
-        "title": "Biogas aus Hofd\u00fcnger in der Schweiz",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814620-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814620-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biogas-aus-hofdunger-in-der-schweiz_1.0",
-        "description": "Ziel dieses Whitepapers ist es, Entscheidungstr\u00e4gern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verf\u00fcgung zu stellen, um die optimale Nutzung von Bioenergie aus Hofd\u00fcnger in der Schweizer Energiewende zu f\u00f6rdern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums f\u00fcr Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erw\u00e4hnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.",
-        "license": "proprietary"
-    },
-    {
-        "id": "biogas-from-animal-manure-in-switzerland_1.0",
-        "title": "Biogas from animal manure in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814639-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814639-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biogas-from-animal-manure-in-switzerland_1.0",
-        "description": "Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.",
-        "license": "proprietary"
-    },
-    {
-        "id": "biomass_above_ground_of_live_trees-19_1.0",
-        "title": "Biomass above ground of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814656-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814656-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_above_ground_of_live_trees-19_1.0",
-        "description": "Dry weight (mass) of the aboveground parts of living trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "biomass_allocation_703_1",
         "title": "Biomass Allocation and Growth Data of Seeded Plants",
@@ -203969,71 +200758,6 @@
         "description": "This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric  additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious",
         "license": "proprietary"
     },
-    {
-        "id": "biomass_of_live_trees-18_1.0",
-        "title": "Biomass of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814713-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814713-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_of_live_trees-18_1.0",
-        "description": "Dry weight (mass) of living trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "biomass_of_lying_dead_trees-70_1.0",
-        "title": "Biomass of lying dead trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814786-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814786-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_of_lying_dead_trees-70_1.0",
-        "description": "Dry weight (mass) of dead, lying trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition of the stem, the branch coarse wood.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "biomass_of_lying_dead_wood_lis-72_1.0",
-        "title": "Biomass of lying dead wood (LIS)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814909-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814909-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_of_lying_dead_wood_lis-72_1.0",
-        "description": "Dry weight (mass) of lying deadwood  starting at 7 cm in diameter that does not fulfil the criteria for a tally tree (measurement location of dbh not identifiable or the dbh is less than 12cm).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "biomass_of_standing_dead_trees-69_1.0",
-        "title": "Biomass of standing dead trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814961-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814961-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_of_standing_dead_trees-69_1.0",
-        "description": "Dry weight (mass) of dead, standing trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition, the branch coarse wood.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "biomass_of_total_dead_wood-71_1.0",
-        "title": "Biomass of total dead wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814991-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814991-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/biomass_of_total_dead_wood-71_1.0",
-        "description": "Dry weight (mass) of all deadwood. This consists of the standing dead trees and shrubs  starting at 12cm dbh and the lying deadwood  starting at 7cm in diameter.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "biomdens_450_1",
         "title": "BOREAS TE-18 Biomass Density Image of the SSA",
@@ -204086,45 +200810,6 @@
         "description": "The dataset was compiled from papers entered into Block's bibliography of invertebrate occurrences in the Antarctic and sub-Antarctic. The dataset provides a comprehensive list of all terrestrial invertebrates recorded from the Antarctic and sub-Antarctic (at that time). Data were entered into an Excel spreadsheet, which contains approximately 3500 entries.  This dataset forms part of the work completed for Australian Antarctic Science (AAS) project 1146 (ASAC_1146) and the RiSCC program, AAS project 1015 (ASAC_1015).  Papers from the Block Bibliography are available as a separate collection in the Australian Antarctic Division Library.  This dataset has also been incorporated into the biodiversity database, which can be found at the provided URL.",
         "license": "proprietary"
     },
-    {
-        "id": "bluegreen-ecological-network-data_1.0",
-        "title": "Multi-Scale Prioritization framework for Urban Blue-Green Infrastructure Planning to Support Biodiversity: Data & Codes",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.7645874, 47.0925656, 9.0719604, 47.6320819",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081654-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081654-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/bluegreen-ecological-network-data_1.0",
-        "description": "This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).",
-        "license": "proprietary"
-    },
-    {
-        "id": "bole_wood_mass_of_live_trees-50_1.0",
-        "title": "Bole wood mass of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814548-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814548-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/bole_wood_mass_of_live_trees-50_1.0",
-        "description": "Dry weight (mass) of the stemwood with bark of the living trees and shrubs  starting at 12 cm dbh.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0",
-        "title": "From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814557-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814557-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0",
-        "description": "Book of abstracts from the  virtual conference \"From Plans to Land Change: Dynamics of Urban Regions\"    Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning\u2019s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.    This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.    The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
     {
         "id": "boreas_aeshrday_235_2",
         "title": "BOREAS AES Canadian Hourly and Daily Surface Meteorological Data, R1",
@@ -204320,19 +201005,6 @@
         "description": "This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM.   The data are stored in a UTM zone 49 projection.  They were created by interpolation of point data using Kriging.  The input point data comprised soundings and terrestrial contour vertices.  THE DATA ARE NOT FOR NAVIGATION PURPOSES.",
         "license": "proprietary"
     },
-    {
-        "id": "bryophyte-observer-bias_1.0",
-        "title": "Greater observer expertise leads to higher estimates of bryophyte species richness",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2024-01-01",
-        "end_date": "2024-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/bryophyte-observer-bias_1.0",
-        "description": "This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relev\u00e9s conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relev\u00e9s as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise.    Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, K\u00fcchler H, Pichon  NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of  bryophyte species richness. _Journal of Vegetation Science_. (submitted)",
-        "license": "proprietary"
-    },
     {
         "id": "bunger_east_sat_1",
         "title": "Bunger Hills East Satellite Image Map 1:50 000",
@@ -205178,19 +201850,6 @@
         "description": "The following was done by a contractor  for the Australian Antarctic Division:  A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created  by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then  (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and   (ii) georeferenced.  See the Quality section for details about the satellite images and the georeferencing.  The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details.  Three satellite image maps were produced from the georeferenced mosaic.  See the SCAR Map Catalogue entries in Related URLs for details.",
         "license": "proprietary"
     },
-    {
-        "id": "carabid-beetles-in-forests_2.0",
-        "title": "Carabid beetles in forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814572-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814572-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/carabid-beetles-in-forests_2.0",
-        "description": "Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.    Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.    Data are provided on request to contact person against bilateral agreement.",
-        "license": "proprietary"
-    },
     {
         "id": "casey_alk_clones_1",
         "title": "Alkane mono-oxygenase genes from marine sediment near Casey",
@@ -205334,45 +201993,6 @@
         "description": "Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 189 V5/584 6610/1 scale 1:25 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.",
         "license": "proprietary"
     },
-    {
-        "id": "catchment-biodiversity-vaud-edna_1.0",
-        "title": "Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "6.8579407, 46.1876586, 7.3303528, 46.6289126",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081799-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081799-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/catchment-biodiversity-vaud-edna_1.0",
-        "description": "This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.",
-        "license": "proprietary"
-    },
-    {
-        "id": "causal-effect-of-lup_1.0",
-        "title": "Causal effect of LUP",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "116.4484859, 23.9449666, 118.6127926, 25.7295192",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814593-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814593-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/causal-effect-of-lup_1.0",
-        "description": "Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.    Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.     Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.   XZQDM2 is villages' unique administrative ID;  Area is the land area of village i;  Dis2water is the Euclidean distance from village i to the nearest waterbody;  Dis2coastl is the Euclidean distance from village i to the nearest coastline;  Dis2city is the the Euclidean distance from village i to the city center;  Dis2county is the the Euclidean distance from village i to the nearest county center;  Elevation is the the average elevation within village i;  Dis2road is the the Euclidean distance from village i to the nearest road;  Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;  Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;  Intensity is the percentage of land that was assigned to the development-permitted zones in village i;   Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;  BuLE is the dependent variable, representing built-up land expansion in village i in year t;  Town is town' unique administrative ID.     Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend  assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.",
-        "license": "proprietary"
-    },
-    {
-        "id": "causal-effect-of-mfoz_1.0",
-        "title": "Causal effect of MFOZ",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "116.2441398, 23.5080862, 120.7485344, 27.512657",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814608-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814608-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/causal-effect-of-mfoz_1.0",
-        "description": "Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?    Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013\u20132015, 2013\u20132018 and 2013\u20132020).     Data: Data.xlsx contains the variables of 954 towns in Fujian Province.   Town_ID is the town unique ID;   County_ID is the county unique ID;  City_ID is the city unique ID;  MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);  Builtup_13_15 is the built-up land expansion from 2013 to 2015;   Builtup_13_18 is the built-up land expansion from 2013 to 2018;  Builtup_13_20 is the built-up land expansion from 2013 to 2020;  Dis2water is the Euclidean distance from the town to the nearest waterbody;  Slope is the the average slope within the town;  GDP is the average GDP in 2010 within the town;   Pop is the average population in 2010 within the town;  Road is the average population in 2010 within the town;  Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;  Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.    Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013\u20132015, 2013\u20132018 and 2013\u20132020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.",
-        "license": "proprietary"
-    },
     {
         "id": "cb54bd70826842a9acf658ebabe4a104_NA",
         "title": "ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1",
@@ -205425,19 +202045,6 @@
         "description": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10\u00c2\u00b0 latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10\u00c2\u00b0 latitude zones from 90\u00c2\u00b0S to 90\u00c2\u00b0N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \u00e2\u0080\u009cESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc\u00e2\u0080\u009d contains monthly zonal mean data for ACE in 2008.",
         "license": "proprietary"
     },
-    {
-        "id": "ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0",
-        "title": "CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.806475, 46.832964, 9.806475, 46.832964",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814623-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814623-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0",
-        "description": "__Cloud Condensation Nuclei (CCN) data:__ A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).    __Hygroscopicity data:__ The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying K\u00f6hler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).    __Predicted cloud droplet numbers:__ Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the \u201ccharacteristic velocity\u201d approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).",
-        "license": "proprietary"
-    },
     {
         "id": "cdcb0605afa74885a66d8be0fdd2ed24_NA",
         "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ensemble product), Version 2.6",
@@ -205490,19 +202097,6 @@
         "description": "This GIS dataset was derived from a detail survey of Cape Denison, Antarctica by G. Crispo in December 1985. Features include coastline, contours, buildings and structures, lakes, areas of exposed rock and penguin colonies.  See AAD File 00/802.",
         "license": "proprietary"
     },
-    {
-        "id": "ceilometer-klosters_1.0",
-        "title": "Ceilometer Klosters",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.880413, 46.869019, 9.880413, 46.869019",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814641-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814641-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/ceilometer-klosters_1.0",
-        "description": "Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.",
-        "license": "proprietary"
-    },
     {
         "id": "century_vemap_m4_820_1",
         "title": "CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)",
@@ -205529,32 +202123,6 @@
         "description": "This dataset contains optical ice velocity time series and seasonal product of the Jakobshavn Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-03 and 2017-09-08.  It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.",
         "license": "proprietary"
     },
-    {
-        "id": "ch2014_1",
-        "title": "Alpine3D simulations of future climate scenarios CH2014",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "8.227, 46.79959, 8.227, 46.79959",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/ch2014_1",
-        "description": "# Overview    The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.    # Experimental Setup    Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graub\u00fcnden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m \u00d7 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999\u20132012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).    # Data    Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5\u20139 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400\u2013800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.",
-        "license": "proprietary"
-    },
-    {
-        "id": "challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0",
-        "title": "Challenging the sustainability of urban beekeeping: evidence from Swiss cities",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814684-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814684-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0",
-        "description": "Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations)  in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2)  (Dataset 2) aggregated data to model the sustainability of urban beekeeping.",
-        "license": "proprietary"
-    },
     {
         "id": "charter-mux-1_NA",
         "title": "CHARTER - MUX",
@@ -205607,58 +202175,6 @@
         "description": "Satellite image map of Charybdis Glacier, Mac. Robertson Land, Antarctica. This map is part (c) in a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991.  The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot/tracks, stations/bases, and glaciers/ice shelves. The map has only geographical co-ordinates.",
         "license": "proprietary"
     },
-    {
-        "id": "chelsa-climatologies_2.1",
-        "title": "Climatologies at high resolution for the earth\u2019s land surface areas",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814735-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814735-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/chelsa-climatologies_2.1",
-        "description": "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth\u2019s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30\u2009arc\u2009sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.   &nbsp;    CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.     __Paper Citation:__   > _Karger DN. et al. Climatologies at high resolution for the earth\u2019s land surface areas, Scientific Data, 4, 170122 (2017)  [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._",
-        "license": "proprietary"
-    },
-    {
-        "id": "chelsa_cmip5_ts_1.0",
-        "title": "High resolution monthly precipitation and temperature timeseries for the period 2006-2100",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "180, -90, -180, 84",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814811-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814811-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/chelsa_cmip5_ts_1.0",
-        "description": "Predicting future climatic conditions at high spatial resolution is essential for many applications in science. Here we present data for monthly time series of precipitation and minimum and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation sums at ~5km spatial resolution globally for the years 1850-2100. We validated the performance of the downscaling algorithm by comparing model output with observed climates for the years 1950-2069.     CHELSA_cmip5_ts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.",
-        "license": "proprietary"
-    },
-    {
-        "id": "chelsa_trace_1.0",
-        "title": "CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "179.995693, -89.9959722, -179.9959722, 83.9956937",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814958-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814958-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/chelsa_trace_1.0",
-        "description": "High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.     The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.",
-        "license": "proprietary"
-    },
-    {
-        "id": "chelsacruts_1.0",
-        "title": "CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814896-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814896-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/chelsacruts_1.0",
-        "description": "CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum.  Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.    CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.",
-        "license": "proprietary"
-    },
     {
         "id": "chem_26_1",
         "title": "Canopy Chemistry (OTTER)",
@@ -205698,32 +202214,6 @@
         "description": "The variation in the phytoplankton biomass over a decadal time scale, and its relationship with the Antarctic Circumpolar Wave (ACW) and climate change, has been poorly interpreted because of the limited satellite chlorophylla (chl a) data compared with the physical parameters from satellite.  We analysed a long-term chl a dataset along the Japanese Antarctic Research Expedition (JARE) cruise tracks since 1965 to investigate inter-annual variation of phytoplankton biomass.  In the Southern Ocean, increasing trends of chl a and the spreading of higher chl a area to the north with 3-7 year cycles were found.  Although relationships between the decadal change in chl a and climate change such as variation of sea ice extent and the El Nino are still obscure, large variation of primary production in proportion to the chl a is implied.  The chl a concentration of sea surface water has been measured routinely on board the icebreakers Fuji and Shirase during almost every cruise of the JARE.  The download file contains chlorophyll a data collected from ship tracks on JARE voyages between 1965 and 2002.  The field in this dataset are:  Date (local time) Year Latitude Longitude Corrected Chlorophyll a  See the attached paper for more details.  The publications on the data collected during the 1965-1976 and 1988-1993 cruises are listed in Fukuchi [1980] and Suzuki and Fukuchi [1997], respectively.  For data on the 1977-1985 and 1994-1997 cruises, see [Kanda and Fukuchi, 1979; Fukuchi and Tamura, 1982; Tanimura, 1981; Watanabe and Nakajima, 1983; Ino and Fukuchi, 1984; Sasaki, 1984; Hamada et al., 1985; Fukuda et al., 1986; Hattori and Fukuchi, 1988; Midorikawa et al., 2000]. Data post 1998-2002 cruises is in Hirawake and Fukuchi [2004].  Data from the 1986-1987 will be published in the JARE data report of digital media, including all cruise data.  Auxiliary Material for paper 2004GL021394 Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002. Toru Hirawake, Tsuneo Odate and Mitsuo Fukuchi (National Institute of Polar Research, Tokyo) Geophys. Res. Lett., Vol (Num), doi:10.1029/2004GL021394   All of the chl a data have been reported in the publications of the National Institute of Polar Research (NIPR).",
         "license": "proprietary"
     },
-    {
-        "id": "chm-hp-4rtm_1.0",
-        "title": "Forest canopy structure data for radiation and snow modelling (CH/FIN)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.871859, 46.845432, 26.6365886, 67.366827",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814990-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814990-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/chm-hp-4rtm_1.0",
-        "description": "This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankyl\u00e4, Finland. Data include:     * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each)  * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2)      These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication:    _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_    This publication must be cited when using the data.     ### See also:   For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy)    The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572)",
-        "license": "proprietary"
-    },
-    {
-        "id": "climate-change-scenarios-at-hourly-resolution_1.0",
-        "title": "Dataset for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814547-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814547-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/climate-change-scenarios-at-hourly-resolution_1.0",
-        "description": "In fall 2019, a new set of climate change scenarios has been released for   Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are  provided at daily resolution. We produced from the CH2018 dataset a new   set of climate change scenarios temporally downscaled at hourly resolution.   In addition, we extended this dataset integrating the meteorological stations   from the Inter-Cantonal Measurement and Information System (IMIS) network,   an alpine network of automatic meteorological stations operated by the WSL   Institute for Snow and Avalanche Research SLF.    The extension to the IMIS network is obtained using a Quantile Mapping approach   in order to perform a spatial transfer of the CH2018 scenarios from the location   of the  MeteoSwiss stations to the location of the IMIS stations. The temporal   downscaling is performed using an enhanced Delta-Change approach. This approach   is based on objective criteria for assessing the quality of the determined delta   and downscaled time series. In addition, this method also fixes a flaw of common   quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling)   related to the decrease of correlation between different variables. The idea behind   the delta change approach is to take the main seasonal signal (and mean) from   climate change scenarios at daily resolution and to map it to a historical time series   at hourly resolution in order to modify the historical time series. The obtained time   series exhibit the same seasonal signal as the original climate change time series,   while it keeps the sub-daily cycle from the historical time series.    The applied methods (Quantile Mapping and Delta-Change) have limitations in   correctly representing statistically extreme events and changes in the frequency   of discontinuous events such as precipitation. In addition, the sub-daily cycle in   the data is inherited from the historical time series, so there is no information   of the climate change signal in this sub-daily cycle. A careful reading of the paper   accompanying the dataset is necessary to understand the limitations and scope of   application of this new dataset.    This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).",
-        "license": "proprietary"
-    },
     {
         "id": "climate_iceberg_1",
         "title": "Antarctic CRC and Australian Antarctic Division Climate Data Set - Australian iceberg observations",
@@ -205776,19 +202266,6 @@
         "description": "This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002.",
         "license": "proprietary"
     },
-    {
-        "id": "climatological-snow-data-1998-2022-oshd_1.0",
-        "title": "Climatological snow data since 1998, OSHD",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/climatological-snow-data-1998-2022-oshd_1.0",
-        "description": "This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).",
-        "license": "proprietary"
-    },
     {
         "id": "climwat_Not provided",
         "title": "CLIMWAT, A Climatic Database",
@@ -205932,19 +202409,6 @@
         "description": "Six GPS data points collected by Alfred Wilklemayer, taken during a one year expedition at Commonwealth Bay, Antarctica.  GPS Points collected at Commonwealth Bay, Antarctica, during 1997  The following GPS data points were collected opportunistically by Mr Alfred Wilklemayer, during a one year expedition in Commonwealth Bay, Antarctica.   Identification Object\tPosition Frozen Husky Dog 67 degrees 04'07\" S, 142 degrees 42'39\" E First Canister 67 degrees 03'69\" S, 142 degrees 42'10\" E Second Canister 67 degrees 03'74\" S, 142 degrees 42'10\" E Third Can/Stick 67 degrees 03'28\" S, 142 degrees 42'09\" E Furthest Point In (during expedition) 67 degrees 05'47\" S, 142 degrees 40'02\" E Furthest Point West (during expedition) 67 degrees 04'06\" S, 142 degrees 06'04\" E",
         "license": "proprietary"
     },
-    {
-        "id": "community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0",
-        "title": "Community structure, life-history traits and performance traits of urban cavity-nesting bees annd wasps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "0.2197266, 46.890732, 28.3886719, 59.0864909",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081815-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081815-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0",
-        "description": "# Background  Urban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we provide the data used to investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. To do so, we installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts).  The dataset contains: * The taxonomic metrics on CNBW * The taxonomic metrics on the natural enemies from CNBW * The life-history traits and performance traits",
-        "license": "proprietary"
-    },
     {
         "id": "comp_runoff_monthly_xdeg_994_1",
         "title": "ISLSCP II UNH/GRDC Composite Monthly Runoff",
@@ -205958,71 +202422,6 @@
         "description": "The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.",
         "license": "proprietary"
     },
-    {
-        "id": "content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0",
-        "title": "Content coding of exemption approval decisions for forest clearances",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814566-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814566-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0",
-        "description": "The Federal Office for the Environment (FOEN) is responsible for granting exemptions for forest clearances that in principle are prohibited in Switzerland. Initiators of infrastructure projects have to submit an examption approval request to the cantonal forest administration which has to inform the FOEN. The FOEN thus administers a dataset of forest clearance requests and approval decisions that can be requested there. This dataset contains information on a coding of the content of all the forest clearance requests between 2001 and 2017, that elicits whether the reason for the clearance can be attributed to \"sustainable economy\" objectives such as \"green economy\", \"bioeconomy\" and \"circular economy\".",
-        "license": "proprietary"
-    },
-    {
-        "id": "convection-in-snow_1.0",
-        "title": "snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian\u2013Eulerian two-phase solver for modelling convection of water vapor in snowpacks",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "6.5678716, 46.5207841, 6.5678716, 46.5207841",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-in-snow_1.0",
-        "description": "snowpackBuoyantPimpleFoam is a  two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).",
-        "license": "proprietary"
-    },
-    {
-        "id": "convection-in-snow_1.0",
-        "title": "snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian\u2013Eulerian two-phase solver for modelling convection of water vapor in snowpacks",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "6.5678716, 46.5207841, 6.5678716, 46.5207841",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/convection-in-snow_1.0",
-        "description": "snowpackBuoyantPimpleFoam is a  two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).",
-        "license": "proprietary"
-    },
-    {
-        "id": "core_0.1",
-        "title": "Cloud Optimized Raster Encoding (CORE) format",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4546554, 47.3605425, 8.4546554, 47.3605425",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814594-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814594-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/core_0.1",
-        "description": "__DISCLAIMER__: CORE is still in development. Interested parties are warmly invited to join common development, to comment, discuss, find bugs, etc.    __Acknowledgement:__ The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing \u200bHTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).    __License:__ The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.    -----------------------    __Summary:__  The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).    __WARNING__: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').    With this early public sharing of the format we explicitly support the Open Science agenda, which implies __\"shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process\"__ (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).    __CORE Specifications:__     1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)    2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)    Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).    3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;     Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.    4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)     5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer    6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)    7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)    __Major issues to be solved:__    - Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.    - Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.    - Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)    __Example__    __Notice__: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].     For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).    For full reproducibility, we provide the original data set and results, as well scripts for data encoding and extraction (see resources).",
-        "license": "proprietary"
-    },
-    {
-        "id": "correct-observer-bias-only-sdms_1.0",
-        "title": "Novel methods to correct for observer and sampling bias in presence-only species distribution models",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "4.9658203, 42.7416347, 17.5341797, 48.2197941",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814610-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814610-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/correct-observer-bias-only-sdms_1.0",
-        "description": "Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.     Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1\u2019900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.    Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r\u22430.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.",
-        "license": "proprietary"
-    },
     {
         "id": "cosmirimpacts_1",
         "title": "Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IMPACTS V1",
@@ -206036,19 +202435,6 @@
         "description": "The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IMPACTS dataset consists of brightness temperature measurements collected by the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) flown onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. CoSMIR is a conical and cross-track scanning radiometer with frequencies centered at 50.3, 52.8, 89.0, 165.5, 183.31\u00b11, 183.31\u00b13, and 183.31\u00b17 GHz. The brightness temperature data from CoSMIR are available from January 15, 2020 through February 28, 2022 in netCDF-4 format.",
         "license": "proprietary"
     },
-    {
-        "id": "cosmo-wrf-documentation_1.0",
-        "title": "Running COSMO-WRF on very-high resolution over complex terrain",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "7.31281, 45.4, 10.6311, 48.2535",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814624-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814624-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/cosmo-wrf-documentation_1.0",
-        "description": "This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.",
-        "license": "proprietary"
-    },
     {
         "id": "cossirimpacts_1",
         "title": "Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR) IMPACTS",
@@ -206088,32 +202474,6 @@
         "description": "The Cloud Physics LiDAR (CPL) IMPACTS dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR (CPL) onboard the NASA ER-2 high-altitude research aircraft in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in HDF-5 format from January 15, 2020, through March 2, 2023.",
         "license": "proprietary"
     },
-    {
-        "id": "crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0",
-        "title": "Dataset for \"Dynamic crack propagation in weak snowpack layers: Insights from high-resolution, high-speed photography\"",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.8698783, 46.8076829, 9.8698783, 46.8076829",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814649-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814649-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0",
-        "description": "This data set includes material and results described in the related research article:    Bergfeld, B., van Herwijnen, A., Reuter, B., Bobillier, G., Dual, J., and Schweizer, J.: Dynamic crack propagation in weak snowpack layers: Insights from high-resolution, high-speed photography, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-360, in review, 2021.    # Context:   In order to study crack propagation in weak snowpack layers in great detail, we recorded Propagation Saw Test (PST) experiments using a high-speed camera and applied digital image correlation (DIC) to derive displacement and strain fields in the slab, weak layer, and substrate. We demonstrated the versatility and accuracy of the DIC method by showing measurements from three PST experiments, resulting in slab fracture, crack arrest and full propagation in the related publication.   # Content:  - Supplementary material for related publication  - Ilustrative videos showing crack propagation  - High-speed recordings of the Experiments   (the raw .cine files are available upon request)  Processed Data containing:     - displacement, velocity and acceleration fields for the three PSTs     - speed and touchdown dataset",
-        "license": "proprietary"
-    },
-    {
-        "id": "crack-propagation-speeds-in-weak-snowpack-layers_1.0",
-        "title": "Crack propagation speeds in weak snowpack layers from three events: PST, whumpf and slab avalanche",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.8700437, 46.807722, 9.8700437, 46.807722",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814659-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814659-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/crack-propagation-speeds-in-weak-snowpack-layers_1.0",
-        "description": "For the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into the underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie, for scales between 5 and 25 meters, we used accelerometers placed on the snow surface, and for scales below 5 meters, we performed a Propagation Saw Test. The mean crack speeds ranged from 36 \u00b1 6 to 49 \u00b1 5 m s^{-1}, and did not exhibit scale dependence. Using the Discrete Element Method and the Material Point Method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales.  This dataset includes raw data as well as crack speed estimates from the three crack propagation events.   Where possible, we reproduced these field experiments with numerical models based on Discrete Element Method (DEM, Bobillier and others, 2020 and 2021) and Material Point Method (MPM. Gaume and others, 2018 and Trottet and others, 2021). The input parameters of the models were estimated from the corresponding snow profiles conducted at each test site.    ## The raw data include:  * Propagation Saw Test movie with mechanical fields derived from Digital image Correlation analysis of the recording  * Acceleration data recorded with wireless time synchronized accelerometers placed on the snow surface during crack propagation in a whumpf.  *Video of an artificially triggered avalanche with widespread crack propagation. The video was used to georeference surface cracks in order to estimate crack propagation time and distance, providing crack propagation speed estimates.  * Snow profile recorded at each test site    ## Experimental crack speed estimates include:  * Crack speed evolution within the first meters derived from the Propagation Saw Test.  * Crack speeds estimated from the time delay of the collapse, observed between different accelerometers during crack propagation of a whumpf.  * Crack speed estimates from video analysis of the artificially triggered avalanche.    ## Reproduced crack speeds using the DEM an MPM model:  * Modelled Propagation Saw Test using MPM (2D and 3D system) and DEM.  * Modelled whumpf using MPM (beam and areal configuration)  * Modelled avalanche using MPM (beam and areal configuration)    Beside the movies (mp4 format), all data is either provided as netCDF files or excel sheets (see readme file), depending on the amount of data. A detailed description of the three crack propagation events and how crack speed was derived, can be found in the related publication:        ### References for applied models:   Bobillier, G., B. Bergfeld, A. Capelli, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2020. Micromechanical modeling of snow failure. The Cryosphere, 14(1): 39-49.    Bobillier, G., B. Bergfeld, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2021. Micro-mechanical insights into the dynamics of crack propagation in snow fracture experiments. Scientific Reports, 11: 11711.    Gaume, J., T. Gast, J. Teran, A. van Herwijnen and C. Jiang 2018. Dynamic anticrack propagation in snow. Nature Communications, 9(1): 3047.    Trottet, B., R. Simenhois, G. Bobillier, A. van Herwijnen, C. Jiang and J. Gaume 2021. From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release. EGU General Assembly 2021, Online, 19-30 Apr 2021, EGU21-8253.",
-        "license": "proprietary"
-    },
     {
         "id": "cramer_leemans_637_1",
         "title": "SAFARI 2000 Mean Climatology, 0.5-Deg, 1930-1960, V[ersion]. 2.1 (Cramer and Leemans)",
@@ -206127,19 +202487,6 @@
         "description": "This data set is a subset of Cramer and Leeman's (1999) global mean monthly climatology . The subset is for the area of southern Africa within the following bounds: 5 N to 35 S and 5 E to 60 E. The data are available in ASCII grid and binary image formats.",
         "license": "proprietary"
     },
-    {
-        "id": "cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5",
-        "title": "Cropland and grassland map of Switzerland based on Sentinel-2 data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814690-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814690-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiY29tbXVuaXR5IHN0cnVjdHVyZSwgbGlmZS1oaXN0b3J5IHRyYWl0cyBhbmQgcGVyZm9ybWFuY2UgdHJhaXRzIG9mIHVyYmFuIGNhdml0eS1uZXN0aW5nIGJlZXMgYW5uZCB3YXNwc1wiLFwiRU5WSURBVFwiLFwiY29tbXVuaXR5LXN0cnVjdHVyZS1saWZlLWhpc3RvcnktdHJhaXRzLWFuZC1wZXJmb3JtYW5jZS10cmFpdHMtb2YtdXJiYW4tY25id1wiLFwiMS4wXCIsMzIyNjA4MTgxNSwyXSIsInVtbSI6IltcImNvbW11bml0eSBzdHJ1Y3R1cmUsIGxpZmUtaGlzdG9yeSB0cmFpdHMgYW5kIHBlcmZvcm1hbmNlIHRyYWl0cyBvZiB1cmJhbiBjYXZpdHktbmVzdGluZyBiZWVzIGFubmQgd2FzcHNcIixcIkVOVklEQVRcIixcImNvbW11bml0eS1zdHJ1Y3R1cmUtbGlmZS1oaXN0b3J5LXRyYWl0cy1hbmQtcGVyZm9ybWFuY2UtdHJhaXRzLW9mLXVyYmFuLWNuYndcIixcIjEuMFwiLDMyMjYwODE4MTUsMl0ifQ%3D%3D/cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5",
-        "description": "We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland,  sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.",
-        "license": "proprietary"
-    },
     {
         "id": "cropland_612_2",
         "title": "NPP Cropland: Gridded Estimates For the Central USA, 1982-1996, R1",
@@ -206309,32 +202656,6 @@
         "description": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation.",
         "license": "proprietary"
     },
-    {
-        "id": "daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0",
-        "title": "daily 500m gridded net radiation and soil moisture for Switzerland, 2004",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814737-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814737-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0",
-        "description": "R data set containing R raster objects with 500m gridded daily modeled soil moisture and net radiation covering Switzerland for the year 2004.",
-        "license": "proprietary"
-    },
-    {
-        "id": "daily-solute-and-isotope-of-stream-water-and-precipitation_1.0",
-        "title": "Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.7092058, 47.04259, 8.75655, 47.1507977",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814801-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814801-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/daily-solute-and-isotope-of-stream-water-and-precipitation_1.0",
-        "description": "This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.  The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.  An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242",
-        "license": "proprietary"
-    },
     {
         "id": "daily_precip_est_793_1",
         "title": "SAFARI 2000 Daily Rainfall Estimates, 0.1-Deg, Southern Africa, 1993-2001",
@@ -206348,32 +202669,6 @@
         "description": "The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 401 lines, each with 341 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv.",
         "license": "proprietary"
     },
-    {
-        "id": "dalmolin_thurmodeling1_1.0",
-        "title": "Data for: Understanding dominant controls on streamflow spatial variability to set-up a semi-distributed hydrological model: the case study of the Thur catchment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.5830688, 47.1112614, 9.6377563, 47.6246779",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814894-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814894-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/dalmolin_thurmodeling1_1.0",
-        "description": "This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre\u2013alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two\u2013stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space\u2013time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe.",
-        "license": "proprietary"
-    },
-    {
-        "id": "danger_descriptions_avalanche_bulletin_switzerland_1.0",
-        "title": "How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland?",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.8886719, 45.7984239, 10.5908203, 47.6804285",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814949-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814949-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/danger_descriptions_avalanche_bulletin_switzerland_1.0",
-        "description": "The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020.",
-        "license": "proprietary"
-    },
     {
         "id": "darling_sst_00_Not provided",
         "title": "2000 Seawater Temperatures at the Darling Marine Center",
@@ -206413,279 +202708,6 @@
         "description": "Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine",
         "license": "proprietary"
     },
-    {
-        "id": "data-amphibian-monitoring_1.0",
-        "title": "Data from: Estimation of breeding probbability can make monitoring data more revealing: a case study of amphibians",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814986-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814986-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-amphibian-monitoring_1.0",
-        "description": "This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the \"first\" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 (\"second\" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021).",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-analysis-toolkits_1.0",
-        "title": "Data analysis toolkits",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814544-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814544-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-analysis-toolkits_1.0",
-        "description": "These are condensed notes covering selected key points in data analysis and statistics.  They were developed by James Kirchner for the course \"Analysis of Environmental Data\" at Berkeley in the 1990's and 2000's.  They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!    License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0",
-        "title": "Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.9761734, 46.5670779, 8.9761734, 46.5670779",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081971-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081971-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiY29tbXVuaXR5IHN0cnVjdHVyZSwgbGlmZS1oaXN0b3J5IHRyYWl0cyBhbmQgcGVyZm9ybWFuY2UgdHJhaXRzIG9mIHVyYmFuIGNhdml0eS1uZXN0aW5nIGJlZXMgYW5uZCB3YXNwc1wiLFwiRU5WSURBVFwiLFwiY29tbXVuaXR5LXN0cnVjdHVyZS1saWZlLWhpc3RvcnktdHJhaXRzLWFuZC1wZXJmb3JtYW5jZS10cmFpdHMtb2YtdXJiYW4tY25id1wiLFwiMS4wXCIsMzIyNjA4MTgxNSwyXSIsInVtbSI6IltcImNvbW11bml0eSBzdHJ1Y3R1cmUsIGxpZmUtaGlzdG9yeSB0cmFpdHMgYW5kIHBlcmZvcm1hbmNlIHRyYWl0cyBvZiB1cmJhbiBjYXZpdHktbmVzdGluZyBiZWVzIGFubmQgd2FzcHNcIixcIkVOVklEQVRcIixcImNvbW11bml0eS1zdHJ1Y3R1cmUtbGlmZS1oaXN0b3J5LXRyYWl0cy1hbmQtcGVyZm9ybWFuY2UtdHJhaXRzLW9mLXVyYmFuLWNuYndcIixcIjEuMFwiLDMyMjYwODE4MTUsMl0ifQ%3D%3D/data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0",
-        "description": "## Summary    * Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland    * R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis    ## Data  The dataset included here is the \"natural\" reservoir inflow for the Luzzone reservoir.  Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).    ## Codes  R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:    * GEV (Generalized Extreme Value)    * GPD and GPDd (Generalized Pareto Distribution including declustered version)    * eGPD (extended Generalized Pareto Distribution)    * MEV (Metastatistical Extreme Value)",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-broedlin-cnp_1.0",
-        "title": "Data Broedlin CNP",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.2944336, 49.2539427, 12.3706055, 52.8695717",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814562-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814562-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-broedlin-cnp_1.0",
-        "description": "Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0",
-        "title": "Data, Code Link and Metadata on Forward Scattering of Snow at Totalp",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.8051, 46.82699, 9.83738, 46.83847",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814579-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814579-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0",
-        "description": "### Overview  We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.  Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.    ### Format  The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.     ### Structure  In Data_Forward_Scattering.zip you will find all necessary data and model details to   reproduce the results of the JGR publication \"How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels\"     - \t__Model Input Data__ contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.   -  __Model Code__ contains the full model Alpine3d including the radiative transfer module GROUNDEYE.   -  __Model Output Data__ contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.   -  __Measurements Solar Testsite__ contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.   -  __Postprocessing__ contains all R-Scripts used for the analysis and   plotting of the corresponding data.     In each of these folders you will find detailed information in   the file 'About this Folder.txt'.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-for-huelsmann_et_al_ecol_appl_2016_1.0",
-        "title": "Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "5.8447266, 45.7521934, 24.0023804, 53.917281",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814613-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814613-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-for-huelsmann_et_al_ecol_appl_2016_1.0",
-        "description": "The datasets comprise nearly 19\u2019000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.    ## Inventory data  The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.    ## Mortality predictors  A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.     To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.    ## Further information  For further information, refer to H\u00fclsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-for-numerical-investigation-of-sediment-yield_1.0",
-        "title": "Data for Numerical Investigation of Sediment Yield Underestimation in Supply-Limited Mountain Basins with Short Records",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.3127747, 46.0721826, 7.8923035, 46.4841158",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814626-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814626-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-for-numerical-investigation-of-sediment-yield_1.0",
-        "description": "The dataset contains the input and output files from the publication by Hirschberg et al. (2022).    The input files are the climate forcing time series generated with the AWE-GEN model. The output files include the hydrological outputs, which is the same for scenarios 1-6 considered in Hirschberg et al. (2022), and the sediment-related outputs, whereas the transport-limited scenario 6 is included in the output of scenario 1.    The input file includes:    - time _D_ (h)  - precipitation _Pr_ (mm/h)  - atmospheric temperature _Ta_ (\u00b0C)  - incoming shortwave radiation _Rsw_ (W/m^2)  - cloudiness _N_ (-)    The output files include:    - hydrological outputs (accroding to time in input and normalized by basin area)      - total discharge _Q_ (mm/h)      - surface discharge _Qs_ (mm/h)      - subsurface discharge _Qss_ (mm/h)      - soil water storage _Vw_ (mm)      - snow depth _snow_ (mm SWE)      - snow depth change _snowacc_ (mm/h SWE)      - potential evapotranspiration _PET_ (mm/h)      - actual evapotranspiration _AET_ (mm/h)    - sediment outputs (accroding to time in input and normalized by basin area)      - hillslope landslide magnitude _ls_ (mm/h)      - channel sediment storage _sc_ (mm)      - hillslope sediment storage _sh_ (mm)      - total sediment discharge _so_ (mm/h)      - transport-limited total sediment discharge _sopot_ (mm/h)      - sediment discharge by debris flows _dfs_ (mm/h)      - transport-limited sediment discharge by debris flows _dfspot_",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-from-hagen-skeels-etal-pnas_1.0",
-        "title": "Data from: Earth history events shaped the evolution of uneven biodiversity across tropical moist forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814651-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814651-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/data-from-hagen-skeels-etal-pnas_1.0",
-        "description": "Datasets and R scripts     ~~~~~Datasets    Dataset_S1.csv: Distribution of species diversity in plant and vertebrate clades. Total clade level diversity and species diversity in tropical moist forests (TMF) across the Neotropics, Indomalaya and Afrotropics. Pantropical clades are found in all three TMF regions with at least one-third of the clades\u2019 total diversity spread throughout these regions. Pantropical diversity disparity (PDD) clades show lower diversity in TMF in the Afrotropics than in the Neotropics and Indomalaya.    Dataset_S2.csv: Environmental and species richness data across 110 km x 110 km grid cells in Neotropical, Indomalayan and Afrotropical moist forest sites. Variables include x and y coordinates in the Behrmann equal area coordinate reference system, potential evapotranspiration (PET), mean annual temperature (MAT), mean annual precipitation (MAP), amphibian, mammal, bird and squamate reptile species richness and biogeographic region, as well as the first two principal components of a principal component analysis on PET, MAT and MAP (PC1, PC2).    Dataset_S3.csv: Global reconstructed paleo-temperature estimates and spatial coordinates across 200 million years at 170,000 year intervals at 2 degree spatial resolution.    Dataset_S4.csv: Gen3sis model parameters and biodiversity summary statistics. Summary statistics include the number of extant species, the number of extinct species, the total number of species, the number of species within the tropical moist forest biome boundaries in the Neotropics, the Afrotropics and Indomalaya, the pantropical index, and the pantropical disparity index, as well as the running time-step and diversity of unfinished simulations.    Dataset_S5.csv: Net relatedness index (NRI) values for vertebrate clades showing an observed disparity in pantropical diversity in the Neotropical, Indomalayan and Afrotropical moist forest regions and associated P-values. Positive values indicate phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion.    ~~~~~Scripts    Script_1 - GLS.R. R script to replicate the linear modelling analyses.    Script_2 - Gen3sis_config_template.R. R script to generate the configurations files to run the simulation experiment.    Script_3 - Gen3sis_config_creator.R. R script to generate the configurations files to run the simulation experiment.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-hagenmoos-1989-2020_1.0",
-        "title": "Restoration of the lowland raised bog Hagenmoos (Switzerland): Data on vegetation, ecological indicator values and species richness from 1989 until 2020",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.5183311, 47.2336995, 8.5245109, 47.2365551",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814669-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814669-ENVIDAT.html",
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-        "description": "This dataset includes data from three vegetation surveys in a restored raised bog (Hagenmoos) in the lowland of the canton of Z\u00fcrich (Switzerland). The bog Hagenmoos was restored by cutting shrubs and trees within the formerly peat-cutting pits and by blocking drainages. The vegetation surveys were carried out before (1989), ten years after (1999) and 30 years after restoration (2020). In each vegetation survey, all vascular plant and bryophyte species within 72 permanent plots were recorded. Of these plots, 34 are located within the formerly peat-cutting pits and 38 are located outside the peat pits. Based on presence-absence data of vascular plants and bryophytes, mean ecological indicator and strategy values based on Landolt et al. (2010) were calculated and are provided in the Excel sheet. Indicator values for light, moisture, pH, nutrients, humus, temperature and continentality and strategy values for stress, competition and ruderality were considered. Furthermore, species richness for the following groups were calculated: (1) all plant species, (2) all vascular plant species, (3) bog specialists among vascular plant species, (4) all bryophyte species, (5) bog specialists among bryophyte species. As bog specialist species, we considered all plant species listed as characteristic species of raised bogs by Feldmeyer-Christe and K\u00fcchler (2018: Moore der Schweiz. Haupt, Bern).",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-of-national-dishes-their-similarity-and-trade-flows_1.0",
-        "title": "Data of national dishes, their similarity and trade flows",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814795-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814795-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIiwidW1tIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIn0%3D/data-of-national-dishes-their-similarity-and-trade-flows_1.0",
-        "description": "The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0",
-        "title": "Data on wild bee taxonomic and functional diversity in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081828-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081828-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0",
-        "description": "Raw data supporting the paper \"Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients\". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:  - Alpha taxonomic community metrics: species richness and Shannon diversity  - Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.  - Community weighted means of 8 functional traits   - The local community contributions on the functional and taxonomic beta diversity (LCBD).    The dataset also includes the following:  - The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).  -The three types of protected areas, defined according to the protective measures.  - The model evaluation, variable importance and partial dependece data.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-set-of-mee-20-04-264_1.0",
-        "title": "Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814549-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814549-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/data-set-of-mee-20-04-264_1.0",
-        "description": "The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper \u201cPlant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources\u201d published in the journal 'Methods in Ecology and Evolution'.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data-snow-instability_1.0",
-        "title": "Data set on snow instability",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.7318268, 46.735343, 9.9652863, 46.8707071",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814573-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814573-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/data-snow-instability_1.0",
-        "description": "These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.    Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:  Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data_ecolappl_2020_1.0",
-        "title": "Grassland restoration: insects and insect traits",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.5198975, 47.4359836, 8.6489868, 47.4944726",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814592-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814592-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/data_ecolappl_2020_1.0",
-        "description": "This dataset contains all data, on which the following publication below is based.    **Paper Citation**:    Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Sch\u00fctz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)    Please cite this paper together with the citation for the datafile.    # Methods    ## Study site  The study area is situated within and nearby to Eigental nature reserve (47\u00b027\u201936\u201d to 47\u00b029\u201906\u201d N, 8\u00b037\u201912\u201d to 8\u00b037\u201944\u201d E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 \u00b1 136 mm and 9.14\u00b0C \u00b1 0.50\u00b0C (mean \u00b1 SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).  In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:    - **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).  - **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).  - **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).    Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altl\u00e4ufe der Glatt; 47\u00b028\u201929\u201d to 47\u00b027\u201941\u201d N, 8\u00b031\u201956\u201d to 8\u00b032\u201926\u201d E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m \u00d7 5 m) spread across the sites. Altogether, the experiment included 55 plots.    ## Arthropod sampling  Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m \u00d7 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),    ## Functional traits  We used two sets of functional traits in this study.      **Morphometric traits**:    Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, \u00a9 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, \u00a9 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.      **Life-history traits**    Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species\u2019 vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species\u2019 optimum habitat moisture, and moisture range, which describes the species\u2019 range of preferable moisture conditions.        ### References    Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensr\u00e4ume der Schweiz: \u00d6kologie - Gef\u00e4hrdung - Kennarten. 3rd ed. Ott, Bern.      Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. K\u00f6hler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.      MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Z\u00fcrich.      Neff, F., N. Bl\u00fcthgen, M. N. Chist\u00e9, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001\u20132015.      Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands\u2014lessons from the distant and recent past. Biological Conservation 104:361\u2013376.      Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754\u2013764.",
-        "license": "proprietary"
-    },
-    {
-        "id": "data_jae_2019_1.0",
-        "title": "Grassland restoration: nematodes and plant communities",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.4766388, 47.397776, 8.751297, 47.5008591",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814728-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814728-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/data_jae_2019_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    __Paper Citation:__   > Resch, M.C., Sch\u00fctz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.    Please cite this paper together with the citation for the datafile.    # Methods  ## Study area and experimental settings  The study was conducted in a nature reserve (Eigental: 47\u00b0 27\u2019 to 47\u00b0 29\u2019 N, 8\u00b0 37\u2019 E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 \u00b0C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Z\u00fcrich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.  The three restoration treatments used were:   i. \u201cHarvest only\u201d: Plots are being mowed two to three times a year and the biomass is removed.  ii. \u201cTopsoil\u201d: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.   iii. \u201cTopsoil+Propagules\u201d: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.   \u201cTopsoil\u201d and \u201cTopsoil+Propagules\u201d plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.   Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as \u201cInitial\u201d, and 11 control plots that represent the targeted state of donor sites for \u201cTopsoil+Propagules\u201d, further referred to as \u201cTarget\u201d. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years).     ## Nematode and plant sampling  Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 \u00b0C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.   We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).   The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, B\u00e4umler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).    ## References  Baudirektion Kanton Z\u00fcrich (2007). 10 Jahre Naturschutz-Gesamtkonzept f\u00fcr den Kanton Z\u00fcrich 1995-2005 \u2013 Stand der Umsetzung. Z\u00fcrich: Baudirektion Kanton Z\u00fcrich.  Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.  Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627  Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9  Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundz\u00fcge der Vegetationskunde (3rd ed.). Wien: Springer.  Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensr\u00e4ume der Schweiz: \u00d6kologie - Gef\u00e4hrdung - Kennarten (3rd ed.). Bern: Ott.  Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4  Landolt, E., B\u00e4umler, B., Erhardt, A., Hegg, O., Kl\u00f6tzli, F., L\u00e4mmler, W., \u2026 Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.  Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.  MeteoSchweiz (2018). Klimabulletin Jahr 2017, Z\u00fcrich: MeteoSchweiz.  Moser, D., Gygax, A., B\u00e4umler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gef\u00e4hrteten Farn- und Bl\u00fctenpflanzen der Schweiz. Bern: BUWAL.  Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.   Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004",
-        "license": "proprietary"
-    },
-    {
-        "id": "data_wet_aval_model_1.0",
-        "title": "Weather, snowpack and avalanche occurrence data for automated prediction of wet-snow avalanche activity",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081504-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081504-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/data_wet_aval_model_1.0",
-        "description": "Datasets used to implement the wet-snow avalanche activity model presented in the article:    Hendrick, M., Techel, F., Volpi, M., Olevski, T., P\u00e9rez-Guill\u00e9n, C., van Herwijnen, A., Schweizer, J. (2023). Automated prediction of wet-snow avalanche activity in the Swiss Alps. Journal of Glaciology, under review    Each dataset includes the input variables (weather and snowpack features) and the target variable (wet-snow  avalanche day or not) used to build the model. Additionally, Dataset3_nowcast and Dataset3_forecast contain the predictions provided by the RF12 model.    All input variables are described in the Appendix of the article and also in the read_me file. Further information on SNOWPACK variables is also available at https://models.slf.ch/p/snowpack/ .",
-        "license": "proprietary"
-    },
-    {
-        "id": "database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0",
-        "title": "Database on holdover time of lightning-ignited wildfires",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.4545978, 47.3606372, 8.4545978, 47.3606372",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081111-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081111-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIiwidW1tIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIn0%3D/database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0",
-        "description": "This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.",
-        "license": "proprietary"
-    },
-    {
-        "id": "dataset-for-future-water-temperature_1.0",
-        "title": "Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814970-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814970-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/dataset-for-future-water-temperature_1.0",
-        "description": "This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.  This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).",
-        "license": "proprietary"
-    },
-    {
-        "id": "dataset-for-ogrs-2018-publication_1.0",
-        "title": "Dataset for OGRS 2018 publication",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815000-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815000-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/dataset-for-ogrs-2018-publication_1.0",
-        "description": "This dataset contains the road and plot data used for the geospatial analysis example showcased in \"Fostering Open Science at WSL with the EnviDat Environmental Data Portal\", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.   The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.",
-        "license": "proprietary"
-    },
-    {
-        "id": "dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0",
-        "title": "Dataset of the Socio-cultural Forest Monitoring Switzerland for WaMos2 and WaMos3",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081334-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081334-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0",
-        "description": "This repository consists of the merged data from WaMos2 (2010) and WaMos2 (2020) and also includes both Corona-related surveys that have been conducted within the phase of WaMos3.     WaMos3 is the third assessment of the relationship of the Swiss population to the forest after 1997 and 2010 and was conducted in 2020. As in WaMos2 in 2010, the attitude of the population to the forest as a recreation area, to wood production and to the protective and ecological functions were examined. The topic of climate change was also included. In addition, the views of adolescents between 15 and 18 years of age were taken into account for the first time.     A detailed description of the provided data  can be found in accompanied file \"WaMos_Metadatenbeschreibung_221027.pdf\" which also contains explanations and examples of the merging process from WaMos2 to WaMos3 as well as sampling procedures. Further, the samples itself can be processed with the help of the provided R-file \"EnviDat_WaMos_dataset.R\".",
-        "license": "proprietary"
-    },
-    {
-        "id": "dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0",
-        "title": "Dataset on Cosmo-1 based Energy Potential in Swiss Alps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814559-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814559-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0",
-        "description": "This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01\u00b0 grid cell for the years 2016 and 2017.    100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01\u00b0 horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01\u25e6 pixels within Switzerland amounts to 48657.   Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).     Please cite the following paper in connection with the dataset:    __Paper Citation:__   > _Bert Kruyt, J\u00e9r\u00f4me Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_",
-        "license": "proprietary"
-    },
     {
         "id": "davfair1_gis_1",
         "title": "Davis RAN Fair Sheet Data from HI 171 V5/519-6877/9 scale 1:5000",
@@ -206985,45 +203007,6 @@
         "description": "This one-degree latitude/longitude spatial resolution data set of Methane Emission from Animals data set was compiled at the NASA/Goddard Institute of Space Studies (GISS) from nine animal population densities.*  The statistics on animal populations came from the Food and Agricultural Organization (FAO) and other sources.  The animals were distributed across a one-degree latitude/longitude grid of national political boundaries, and sub-national boundaries for Australia, Brazil, Canada, China, India, USA and the former USSR.  Published estimates of methane production from each type of animal were applied to the populations to yield a global distribution of annual methane emission by animals, expressed in kilograms per square kilometer of CH4 produced annually.   A large spatial variability in the distribution of methane production (and the source animal populations) can clearly be seen in the global digital map.  The total annual global estimate of methane emission is 75.8 teragrams (10 to the 12th power), about 55% of which is found between 25 degrees North and 55 degrees North latitude, a significant contribution to the observed north-south gradient of atmospheric methane concentration.  The proper reference to this data set is \"J. Lerner, E. Matthews and I. Fung, June 1988.  Methane Emission from Animals: a Global High-Resolution Data Base, Global Biogeochemical Cycles, vol. 2, no. 2, pp. 139-156.\"   The original magnetic tape containing these data came from the National Center for Atmospheric Research (NCAR-Scientific Computing Division/Data Support Section); 1850 Table Mesa Drive; Boulder, Colorado; 80307 USA. This tape contains the methane emission data file and ten animal population density data files (the nine listed below plus bovines, a combination of 'cattle' and 'dairy cows').  In addition it has three listing or program files; all of the data and non-data files are in ASCII format.  While all of the 14 files have been read from tape to disk at GRID-Geneva, only the annual methane emission (kg./sq. km.) data file has been converted to a binary image format.   This data set is available as five different file types:    - ASCII file of complex real (floating-point, 32-bit) numbers,      both original file; and the IBM-compatible file;  - 16-bit, signed integer file;  - eight-bit unsigned integer file;  - demonstration file (also eight-bit), useful only for visualization.   Type number (3) is recommended for most analytical purposes, as it contains all of the numerical information of the original file (1), but is easier to work on.  Type number (4) is only recommended for those systems which cannot handle 16-bit data, and type (5) in cases where an annotated image or photoproduct only is desired.   The Methane data file is held in the Plate Carree (Simple Cylindrical) projection, has a spatial resolution of one degree latitude/longitude and consists of 180 rows (lines) by 360 columns (elements/pixels/ samples) of data.  Its origin point is at 90 degrees North latitude and 180 degrees West longitude, and it extends to 90 degrees South latitude and 180 degrees East longitude.  The two-byte or 16-bit per element data file comprises 130 Kb, and the one-byte file 65 kb.    - Cattle, Dairy cows, Water buffalo, Sheep, Goats, Camels, Pigs, Horses and Caribou ",
         "license": "proprietary"
     },
-    {
-        "id": "deadwood-generator_1.0",
-        "title": "Deadwood Generator",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.9440727, 47.0239773, 9.011364, 47.0448028",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081551-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081551-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/deadwood-generator_1.0",
-        "description": "The here presented code generates discrete three-dimensional, RAMMS::ROCKFALL readable deadwood log files (.pts-format) of windtrown forests, including the pilling effect due to slightly different throw directions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "debris-flow-prediction-based-on-rainfall_1.0",
-        "title": "Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "7.5740433, 46.2496507, 7.6626205, 46.3345789",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814606-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814606-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic25vd3BhY2tidW95YW50cGltcGxlZm9hbTogYW4gb3BlbmZvYW0gZXVsZXJpYW4gZXVsZXJpYW4gdHdvLXBoYXNlIHNvbHZlciBmb3IgbW9kZWxsaW5nIGNvbnZlY3Rpb24gb2Ygd2F0ZXIgdmFwb3IgaW4gc25vd3BhY2tzXCIsXCJFTlZJREFUXCIsXCJjb252ZWN0aW9uLWluLXNub3dcIixcIjEuMFwiLDI3ODk4MTQ1ODAsN10iLCJ1bW0iOiJbXCJzbm93cGFja2J1b3lhbnRwaW1wbGVmb2FtOiBhbiBvcGVuZm9hbSBldWxlcmlhbiBldWxlcmlhbiB0d28tcGhhc2Ugc29sdmVyIGZvciBtb2RlbGxpbmcgY29udmVjdGlvbiBvZiB3YXRlciB2YXBvciBpbiBzbm93cGFja3NcIixcIkVOVklEQVRcIixcImNvbnZlY3Rpb24taW4tc25vd1wiLFwiMS4wXCIsMjc4OTgxNDU4MCw3XSJ9/debris-flow-prediction-based-on-rainfall_1.0",
-        "description": "This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).    ## How to install and run the example    Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:    `$ conda env create -f environment.yml`    `$ conda activate thresholds` or `$ source activate thresholds`    To run an example:    `$ python run_example()`    It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.    ## License  GNU General Public License v3.0",
-        "license": "proprietary"
-    },
-    {
-        "id": "debris-flow-volumes-at-the-illgraben-2000-2017_1.0",
-        "title": "Debris-flow volumes at the Illgraben 2000-2017",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "7.5884628, 46.2512413, 7.6440811, 46.3205203",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814621-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814621-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/debris-flow-volumes-at-the-illgraben-2000-2017_1.0",
-        "description": "Debris-flow bulk volumes from the WSL monitoring station.  More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).",
-        "license": "proprietary"
-    },
     {
         "id": "deglacial_water_isotope_composite_gicc05_1",
         "title": "Antarctic Ice Core Deglacial Water Isotope Composite Record on GICC05",
@@ -207063,149 +203046,6 @@
         "description": "This data set contains monthly mean values of diffuse irradiance fraction [f(Ediff), or ratio of diffuse-to-total irradiance] at ground level for a 30-degree solar zenith angle and in seven spectral bands (MODIS bands 1-7) as well broadband visible (400-700 nm), near-infrared (700-3000 nm) and shortwave (400-3000 nm). Values are provided for eight SAFARI 2000 core sites, including Ghanzi/Okwa River Crossing, Maun (Main and Floodplain Towers), Pandamatenga, and Tshane, Botswana; Skukuza, South Africa; Etosha National Park, Namibia; and Mongu, Zambia. The fractions were estimated with the 6S radiative transfer model, given the mean aerosol optical depth (AOT) values from AERONET sunphotometer measurements. Where sunphotometers were not deployed at a SAFARI 2000 core site, the nearest neighbor sunphotometer data were used. A rough estimate of the likely spatial extrapolation error is provided. These data can be used to estimate typical surface albedo (blue sky conditions) from the theoretical black-sky and white-sky albedo values provided in the MODIS albedo product (MOD43), as well as in other applications.Data for all eight sites are contained in one ASCII file, in csv format. The data file provides the ratio of diffuse (atmospherically-scattered) irradiance to total irradiance, both at ground level, for the eight sites in southern Africa. Mean values are provided for each of 12 months in 10 spectral bands between 0.4 and 4.0 microns, computed for a 30-degree solar zenith angle.  The native resolution of the AERONET sunphotometer data varies, but is typically less than 1 hour. Information about the site location, IGBP classification, and nearest AERONET sunphotometer site is also provided.",
         "license": "proprietary"
     },
-    {
-        "id": "digitizing-historical-plague_1.0",
-        "title": "Digitizing historical plague",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-16.171875, 28.3043807, 46.40625, 67.4749224",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814640-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814640-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/digitizing-historical-plague_1.0",
-        "description": "We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.",
-        "license": "proprietary"
-    },
-    {
-        "id": "dischmex-high-resolution-wrf-simulations-and-measurements_1.0",
-        "title": "DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.4070435, 46.3969575, 10.5661011, 47.164322",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814655-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814655-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/dischmex-high-resolution-wrf-simulations-and-measurements_1.0",
-        "description": "The data presented here corresponds to the publication \"Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain\" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication \"The importance of near-surface winter precipitation processes in complex alpine terrain\" (Gerber et al., 2018b). The dataset contains:    * WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).  * StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.  * ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (B\u00fchler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.    For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).    __Publications:__    B\u00fchler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229\u2013243, doi:10.5194/tc-9-229-2015, 2015.    Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137\u20133160, doi:10.5194/tc-12-3137-2018, 2018.    Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.    Gerber, F., and Sharma, V.:  Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.",
-        "license": "proprietary"
-    },
-    {
-        "id": "dischmex-meteorological-measurements_1.0",
-        "title": "DISCHMEX - Meteorological measurements",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.92665, 46.71291, 9.92665, 46.71291",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814692-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814692-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/dischmex-meteorological-measurements_1.0",
-        "description": "Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.    Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).  In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.",
-        "license": "proprietary"
-    },
-    {
-        "id": "disdrometer-data-davos-wolfgang_1.0",
-        "title": "Disdrometer Data Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814759-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814759-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/disdrometer-data-davos-wolfgang_1.0",
-        "description": "The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.",
-        "license": "proprietary"
-    },
-    {
-        "id": "disdrometer-data-gotschna_1.0",
-        "title": "Disdrometer Data Gotschnagrat",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.849, 46.859, 9.849, 46.859",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814886-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814886-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/disdrometer-data-gotschna_1.0",
-        "description": "A laser optical disdrometer (Parsivel\u00b2 , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.",
-        "license": "proprietary"
-    },
-    {
-        "id": "disdrometer_laret_1.0",
-        "title": "Disdrometer Data Laret",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.871859, 46.845432, 9.871859, 46.845432",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814960-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814960-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/disdrometer_laret_1.0",
-        "description": "A laser optical disdrometer (Parsivel&#178; , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.   The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.",
-        "license": "proprietary"
-    },
-    {
-        "id": "dispersal-prevalence-fish-traits-assemblages_1.0",
-        "title": "Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "179.0593033, -27.6032369, -160.5813217, 29.000934",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814989-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814989-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/dispersal-prevalence-fish-traits-assemblages_1.0",
-        "description": "This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. \"A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages\" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.     The interaction of habitat dynamics with species dispersal abilities could generate gradients  in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.",
-        "license": "proprietary"
-    },
-    {
-        "id": "distributed-subcanopy-datasets_1.0",
-        "title": "Distributed sub-canopy datasets from mobile multi-sensor platforms (CH / FIN, 2018-2019) for hyper-resolution forest snow model evaluation",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "20.0170898, 66.8507192, 22.1264648, 68.2270448",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815033-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815033-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/distributed-subcanopy-datasets_1.0",
-        "description": "This dataset contains datasets of sub-canopy meteorological variables acquired in coniferous forest stands in Switzerland (Davos, Engadine) and Finland (Sodankyl\u00e4) during the winters 2018 and 2019. The data are presented and used in the publication:   Mazzotti, G., Essery, R., Webster, C., Malle, J.,  & Jonas T. (2020)  Process-level evaluation of a high-resolution forest snow model using observations from mobile multi-sensor platforms   Water Resources Research, under review    The above publication must be cited when using this dataset, and the user is referred to the publication for additional detail.     Data are grouped into 4 folders:   1) Point data includes wind speed data measured with stationary meteorological stations  2) Transect data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired with an automated calblecar system along within-stand transects  3) Grid data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired on 40x40m gridded plots using a handheld instrument, as well as snow depth data measured at the same grids.     Canopy structure information derived from hemispherical images is included for each all surveyed locations as well, and an overview of the field sites is provided.",
-        "license": "proprietary"
-    },
-    {
-        "id": "distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0",
-        "title": "Distribution maps of permanent grassland habitats for Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081223-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081223-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0",
-        "description": "We modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances according to the Swiss habitat typology (TypoCH; Delarze et al. 2015) at 10x10 m resolution across Switzerland. The 20 grassland habitat types belong to the following habitat groups: fens, wet meadows, raised bogs, re-seeded and heavy fertilized grasslands, dry grasslands, nutrient-poor alpine and subalpine grasslands, nutrient-rich pastures and meadows as well as fallow grasslands.     We followed a two-step approach:     (1) Ensemble models provide **distribution maps of the 20 individual grassland habitat types**, using training data from various sources. Predictors were Copernicus Sentinel satellite imagery and variables describing climate, soil and topography. The performance of these maps was assessed with the True Skill Statistics and split\u2010sampling of the data.     Available maps for each grassland habitat: (1) *Map of the median of predicted probability of occurrence*; (2) *Map of the standard deviation of the predicted probability of occurrence* (available upon request); (3) *Binary presence/absence map* (available upon request). For an overview, see *Overview: Maps of the individual grassland habitats*.    (2) **Combined maps**: The individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert\u2010based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat\u2010type proportions with extrapolations from field surveys.    Available combined maps: Map of the most likely habitat type (M1F; after regional corrections); Map of the second most likely habitat type (M2); Map of the most likely habitat type without regional corrections (available upon request); Map of the weighted median of the predicted probability of occurrence for the most/second most likely habitat type, respectively (available upon request); map of the ratio of the probabilities of occurrence of the most and second most likely habitat types (available upon request)",
-        "license": "proprietary"
-    },
-    {
-        "id": "diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0",
-        "title": "Diversity of ground beetles and spiders as well as cynipid oak gall formation on irrigated and non-irrigated plots in a dry mixed Scots pine forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.6136971, 46.3021928, 7.6136971, 46.3021928",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814550-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814550-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0",
-        "description": "In the dry Pfynwald forest a long-term experiment of WSL was initiated in 2003 with a set of irrigated and non-irrigated plots. Forest Entomologie WSL made several investigations, one of them on the effect of irrigation (or conversely of drought) on the biodiversity of epigaeic arthropods such as ground beetles and spiders. In addition, its effects were also assessed by counting galls formed by gall wasps on pubescent oak.",
-        "license": "proprietary"
-    },
-    {
-        "id": "diversity_of_woody_species-36_1.0",
-        "title": "Diversity of woody species",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814561-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814561-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/diversity_of_woody_species-36_1.0",
-        "description": "Index based on the number of tree and shrub species  starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species  starting at 12 cm dbh in the upper layer.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "dlhimpacts_1",
         "title": "Diode Laser Hygrometer (DLH) IMPACTS",
@@ -207375,45 +203215,6 @@
         "description": "NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.",
         "license": "proprietary"
     },
-    {
-        "id": "drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0",
-        "title": "Drivers of the microbial metabolic quotient across global grasslands",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "144.140625, -25.6309638, -148.359375, 65.4448709",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081473-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081473-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch Anita C., Zimmermann, Stefan, Sch\u00fctz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography    Please cite this paper together with the citation for the datafile.    The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.",
-        "license": "proprietary"
-    },
-    {
-        "id": "drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0",
-        "title": "Drought alters C footprint of trees in soil: tracking the spatio-temporal fate of 13C labelled assimilates in the soil",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "7.5325012, 46.2542959, 7.6945496, 46.339691",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814585-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814585-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0",
-        "description": "Data from pulse-labelling experiment with 100-year old trees of a naturally dry pine forest exposed to a 15-year-long irrigation experiment. Canopies of 10 trees were labelled for 3 hours with 13CO2 and the fate of this label was traced for one year in stem and soil respiration and in microbial biomass around these trees. Data include (1) microclimatic data and soil respiration rates of the year following pulse labelling. (2) Temporal patterns of the 13C signal and 13C excess in soil respired CO2 and microbial biomass. (3) Spatial distribution of 13C signal in the soil.",
-        "license": "proprietary"
-    },
-    {
-        "id": "drought-and-beech-1000-beech-project_1.0",
-        "title": "Data on multi-year drought impacts on European beech in northern Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.4761963, 47.2866819, 9.1351318, 47.8242201",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081555-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081555-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/drought-and-beech-1000-beech-project_1.0",
-        "description": "This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors  on  mortality  and  drought  legacy  processes.",
-        "license": "proprietary"
-    },
     {
         "id": "dtms0bil_247_1",
         "title": "BOREAS Daedalus TMS Level-0 Imagery: Digital Counts in BIL Format",
@@ -207427,19 +203228,6 @@
         "description": "The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.",
         "license": "proprietary"
     },
-    {
-        "id": "dynamics-of-insect-natural-enemies-of-bark-beetles_1.0",
-        "title": "Dynamics of insect natural enemies of bark beetles",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.1146183, 46.9811854, 9.1285229, 46.9903195",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814602-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814602-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/dynamics-of-insect-natural-enemies-of-bark-beetles_1.0",
-        "description": "In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse.  Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above.  The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting.",
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     {
         "id": "e1c0c34e0cc942898b3626efd1dcc095_NA",
         "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1",
@@ -207765,32 +203553,6 @@
         "description": "This data set for the ISLSCP Initiative II data collection provides meteorology data with fixed, monthly, monthly-6-hourly, 6-hourly, and 3-hourly temporal resolutions. The data were derived from the European Centre for Medium-range Weather Forecasts (ECMWF) near-surface meteorology data set, 40-year re-analysis, or ERA-40 (Simmons and Gibson, 2000), which covers the years 1957 to 2001. The data were processed onto the ISLSCP II Earth grid with a spatial resolution of 1-degree in both latitude and longitude, and span the common ISLSCP II period from 1986 to 1995.The ECMWF forecast system is called the Integrated Forecasting System (IFS) and was developed in co-operation with Meteo-France. For ERA40 it is used with 60 levels from the top of the model at 10 Pa to the lowest level at about 10 m above the surface. There are 46 compressed (.tar.gz) data files with this data set. Each uncompressed file contains space-delimited text (.asc) data files.",
         "license": "proprietary"
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-        "id": "ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0",
-        "title": "Ecological properties of urban ecosystems. Biodiversity dataset of Zurich",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
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-        "description": "Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.  Data are provided on request to contact person against bilateral agreement.",
-        "license": "proprietary"
-    },
-    {
-        "id": "ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0",
-        "title": "Ecosystem coupling and multifunctionality - exclosure experiment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "10.0270844, 46.59481, 10.3951263, 46.7662842",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814632-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814632-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    __Paper Citation:__   > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Sch\u00fctz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684.  [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).    Please cite this paper together with the citation for the datafile.    #Methods  ##Study sites   The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park\u2019s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park\u2019s foundation (14th century to 1914) (Sch\u00fctz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Sch\u00fctz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Sch\u00fctz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.  ##Experimental design  To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them \u201cL/M/S/I\u201d, \u201cM/S/I\u201d, \u201cS/I\u201d, \u201cI\u201d, \u201cNone\u201d; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.  Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).   The \u201cL/M/S/I\u201d plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the \u201cM/S/I\u201d plots were unfenced, allowing access to all but the large mammals; 2) the \u201cS/I\u201d plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the \u201cI\u201d plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the \u201cNone\u201d plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.  To assess whether the design of the \u201cNone\u201d exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six \u201cmicro-climate control\u201d exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the \u201cNone\u201d exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the \u201cNone\u201d plots, but also a comparable feeding pressure by invertebrates to the \u201cI\u201d plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the \u201cNone\u201d plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the \u201cI\u201d and the \u201cNone\u201d treatments were not due to the construction of the \u201cNone\u201d exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).   ##Aboveground invertebrate sampling  Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the \u201cNone\u201d plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).   We sorted the \u2248100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.  ##Sampling of plant properties   The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65\u00b0C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, F\u00e4llanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 \u00b0C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 \u00b0C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.  ##Sampling of edaphic communities  In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter \u00d7 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4\u00b0C. Again, we only used the 2013 data in this study.  Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].   We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 \u2013 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.   ##Sampling of soil properties  We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 \u00b0C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.   Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenm\u00fchle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 \u00b0C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 \u00b5m) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 \u00b5m cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).   Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.   Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.  ##Numeral calculations and statistical analyses  Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.  We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2 \uf0b4 5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the \u201cNone\u201d plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.   \tAverage ecosystem coupling was calculated as follows:   Ecosystem coupling=  where Xi is the absolute Coupling was calculated value of the Spearman\u2019s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80;  interactions (56 in the case of the \u201cNone\u201d treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the \u201cNone\u201d treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the \u201cNone\u201d treatment).\tCoupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the \u201cNone\u201d treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the \u201cNone\u201d treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the \u201cNone\u201d treatment).   To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.        We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.   We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).    #References    - Anderson J, Domsch K. 1978. A physiological method for the quantitative measurement of microbial biomass in soil. Soil Biol Biochem 10:215\u201321.  - Blackwood CB, Marsh T, Kim S-H, Paul EA. 2003. Terminal Restriction Fragment Length Polymorphism Data Analysis for Quantitative Comparison of Microbial Communities. Appl Environ Microbiol 69:926\u201332. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC143601/  - Bongers T. 1988. De nematoden von Nederland. Schoorl, The Netherlands: Pirola  - Byrnes JEK, Gamfeldt L, Isbell F, Lefcheck JS, Griffin JN, Hector A, Cardinale BJ, Hooper DU, Dee LE, Duffy JE. 2014. Investigating the relationship between biodiversity and ecosystem multifunctionality: Challenges and solutions. Methods Ecol Evol 5:111\u201324.  - Crossley DAJ, Blair JM. 1991. A high-efficiency low-technology Tulgren-type extractor for soil microarthopods. Agric Ecosyst Environ 34:187\u201392.  - Dudley N. 2008. Guidelines for applying protected area managment categories. Gland: IUCN  - Frank DA, McNaughton SJ. 1990. Aboveground biomass estimation with the canopy intercept method: A plant growth form caveat. Oikos 57:57\u201360.  - Haynes AG, Sch\u00fctz M, Buchmann N, Page-Dumroese DS, Busse MD, Risch AC. 2014. Linkages between grazing history and herbivore exclusion on decomposition rates in mineral soils of subalpine grasslands. Plant Soil 374.  - Hodel M, Sch\u00fctz M, Vandegehuchte ML, Frey B, Albrecht M, Busse MD, Risch AC. 2014. Does the aboveground herbivore assemblage influence soil bacterial community composition and richness in subalpine grasslands? Microb Ecol 68:584\u201395.  - Liu WT, Marsh TL, Cheng H, Forney LJ. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol 63:4516\u201322. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC168770/  - Manning P, van der Plas F, Soliveres S, Allan E, Maestre FT, Mace G, Whittingham MJ, Fischer M. 2018. Redefining ecosystem multifunctionality. Nat Ecol Evol 2:427\u201336. https://doi.org/10.1038/s41559-017-0461-7  - Meola M, Lazzaro A, Zeyer J. 2014. Diversity, resistance and resilience of the bacterial communities at two alpine glacier forefields after a reciprocal soil transplantation. Environ Microbiol 16:1918\u201334. https://onlinelibrary.wiley.com/doi/abs/10.1111/1462-2920.12435  - Meyer ST, Koch C, Weisser WW. 2015. Towards a standardized Rapid Ecosystem Function Assessment (REFA). Trends Ecol Evol 30:390\u20137. http://www.sciencedirect.com/science/article/pii/S0169534715000968  - Norman R., Stucki JW. 1981. The determination of nitrate and nitrite in soil extracts by ultraviolet spectrophotometry. Soil Sci Soc Am J 45:347\u201353.  - Ochoa-Hueso R. 2016. Non-linear disruption of ecological interactions in response to nitrogen deposition. Ecology 87:2802\u20132814.  - Oostenbrink M. 1960. Estimating nematode populations by some selected methods. In: Sasser NJ, Jenkins WR, editors. Nematology. Chapel Hill, NC, USA: University of North Carolina Press. pp 85\u2013101.  - R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing  - Risch AC, Haynes AG, Busse MD, Filli F, Sch\u00fctz M. 2013. The response of soil CO2 fluxes to progressively excluding vertebrate and invertebrate herbivores depends on ecosystem type. Ecosystems 16:1192\u2013202.  - Risch AC, Sch\u00fctz M, Vandegehuchte ML, Van Der Putten WH, Duyts H, Raschein U, Gwiazdowicz DJ, Busse MD, Page-Dumroese DS, Zimmermann S. 2015. Aboveground vertebrate and invertebrate herbivore impact on net N mineralization in subalpine grasslands. Ecology 96:3312\u201322.  - Sch\u00fctz M, Risch AC, Achermann G, Thiel-Egenter C, Page-Dumroese DS, Jurgensen MF, Edwards PJ. 2006. Phosphorus translocation by red deer on a subalpine grassland in the Central European Alps. Ecosystems 9:624\u2013633.  - Sch\u00fctz M, Risch AC, Leuzinger E, Kr\u00fcsi BO, Achermann G. 2003. Impact of herbivory by red deer (Cervus elaphus L.) on patterns and processes in subalpine grasslands in the Swiss National Park. For Ecol Manage 181:177\u201388.  - Vandegehuchte ML, van der Putten WH, Duyts H, Sch\u00fctz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-speci\ufb01c. Oikos 126:212\u201323.  - Vandegehuchte ML, Raschein U, Sch\u00fctz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.  - Vandegehuchte ML, Sch\u00fctz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434\u201346.  - Vandegehuchte ML, Trivellone V, Sch\u00fctz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545\u201355.  - Wirthner S, Frey B, Busse MD, Sch\u00fctz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296\u2013302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003  - Zumsteg A, Luster J, G\u00f6ransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552\u201364. https://doi.org/10.1007/s00248-011-9991-8",
-        "license": "proprietary"
-    },
     {
         "id": "ecosystem_roots_1deg_929_1",
         "title": "ISLSCP II Ecosystem Rooting Depths",
@@ -207817,19 +203579,6 @@
         "description": "The major objectives of this project are as follows:  1. To determine the composition and distribution of algal flora from a wide range of habitats, which provide a conductive niche for algal population in Antarctica. 2. To compare the Antarctic and tropical algal flora, in order to determine the degree of species endemism based on evolutionary process. 3. To study the important role of habitat specificity in determining the composition of diatom assemblages. 4. To test the utility and suitability of diatom community structure as indicators of environmental stress.   This is done by: 1. Conducting an ecological survey of microalgal distribution at Australian Antarctic station sites by looking into several types of habitat. 2. Identifying the microalgae samples collected based on morphology using light microscopy and SEM. 3. Comparing the algae community, structure and distribution from the tropics.   The principal milestones of the project are as follows: 1. Information of microalgal distribution at several sites in Antarctica. 2. Collection of microalgae cultures.  3. Completion of identification of Antarctic microalgae.  In collaboration with the Australian Antarctic Division (AAD) we have gone on an expeditions to Australian Antarctic Station of Casey and Davis. Collection of samples was made from various sources such as  water, snow and soil and we have established a list of microalgae species in our collection. Comparative studies on the species diversity and distribution with tropical microalgae communities are being conducted. Physiological studies are currently in progress.",
         "license": "proprietary"
     },
-    {
-        "id": "ect-and-rb-data-switzerland_1.0",
-        "title": "ECT and RB data Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "6.6500243, 45.8050626, 10.5831297, 47.4867706",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814654-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814654-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/ect-and-rb-data-switzerland_1.0",
-        "description": "The data set contains the data used in the publication \"On snow stability interpretation of Extended Column Test results\" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.",
-        "license": "proprietary"
-    },
     {
         "id": "edaa7e7324e849f683d3726088a0c7bd_NA",
         "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 3.1",
@@ -207869,19 +203618,6 @@
         "description": "The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 *.zip data files with this data set.",
         "license": "proprietary"
     },
-    {
-        "id": "edna-fjord-svalbard-fish-plankton_1.0",
-        "title": "Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "10.645752, 78.9769189, 12.689209, 79.4215522",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081772-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081772-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIiwidW1tIjoiW1wiZGF0YSBmcm9tOiBlc3RpbWF0aW9uIG9mIGJyZWVkaW5nIHByb2JiYWJpbGl0eSBjYW4gbWFrZSBtb25pdG9yaW5nIGRhdGEgbW9yZSByZXZlYWxpbmc6IGEgY2FzZSBzdHVkeSBvZiBhbXBoaWJpYW5zXCIsXCJFTlZJREFUXCIsXCJkYXRhLWFtcGhpYmlhbi1tb25pdG9yaW5nXCIsXCIxLjBcIiwyNzg5ODE0OTg2LDddIn0%3D/edna-fjord-svalbard-fish-plankton_1.0",
-        "description": "This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).     # Methods       **Sampling**  We sampled the Lillieh\u00f6\u00f6k fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lillieh\u00f6\u00f6k fjord merged with the mouth of M\u00f6ller fjord. The fjord\u2019s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-\u00b5m pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.      **Molecular**  To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).     # Data content:     + Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.   + Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.       # Additional informations  For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.",
-        "license": "proprietary"
-    },
     {
         "id": "edward_viii_sat_1",
         "title": "Edward VIII Gulf Satellite Image Map 1:100 000",
@@ -207895,19 +203631,6 @@
         "description": "Satellite image map of Edward VIII Gulf, Kemp Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1993.  The map is at a scale of 1:100000, and was produced from a Landsat TM (WRS 139-107) scene (bands 2,3 and 4). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and penguin colonies, and gives some historical text information. The map has both geographical and UTM co-ordinates.",
         "license": "proprietary"
     },
-    {
-        "id": "eemma_1.0",
-        "title": "eemma.R, an R script for Ensemble End-Member Mixing Analysis",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081791-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081791-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/eemma_1.0",
-        "description": "The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.    A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.    The user's guide is available for public use under Creative Commons CC-BY-SA.  Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/",
-        "license": "proprietary"
-    },
     {
         "id": "ef1627f523764eae8bbb6b81bf1f7a0a_NA",
         "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.1",
@@ -207960,32 +203683,6 @@
         "description": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 \u00e2\u0080\u0093 2019. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Mets\u00c3\u00a4m\u00c3\u00a4ki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Mets\u00c3\u00a4m\u00c3\u00a4ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 \u00c2\u00b5m, and an emissive band centred at about 11 \u00c2\u00b5m. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.",
         "license": "proprietary"
     },
-    {
-        "id": "effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0",
-        "title": "Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.8225713, 46.796135, 9.8225713, 46.796135",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081817-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081817-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0",
-        "description": "The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.  The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.  We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy. This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example. Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.",
-        "license": "proprietary"
-    },
-    {
-        "id": "effects-of-canopy-disturbance-on-swiss-forests_1.0",
-        "title": "Effects of canopy disturbance on Swiss forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814791-ENVIDAT.umm_json",
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-        "description": "The files refer to the data used in Scherrer et al. (2021) \"Canopy disturbances catalyse tree species shifts in Swiss forests\" in _Ecosystems_.    The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory.  In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication.    For more detailed information about the data files please check the ReadMe.docx file.",
-        "license": "proprietary"
-    },
     {
         "id": "elev_arc_250_1",
         "title": "BOREAS Elevation Contours over the NSA and SSA ARC/Info Generate Format",
@@ -207999,32 +203696,6 @@
         "description": "Elevation contours over the NSA and SSA in ARC/Info Generate Format.  Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.",
         "license": "proprietary"
     },
-    {
-        "id": "elevation-profiler-first-release_1.0",
-        "title": "elevation-profiler: first release",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "8.4545978, 47.3606372, 8.4545978, 47.3606372",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081954-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226081954-ENVIDAT.html",
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-        "description": "Elevation profiler (see Krebs et al. 2015) is an open source GIS tool designed to work with ArcGIS that automatically calculates transverse or longitudinal elevation profiles of different lengths starting from a digital elevation model (e.g. high resolution Lidar DEM) and a shapefile of points (i.e. the midpoints of the profile segments). The calculated profiles are then saved in comma-separated tabular data files (.csv).    GNU General Public License v2.0 only",
-        "license": "proprietary"
-    },
-    {
-        "id": "elk-and-bison-carcasses-in-yellowstone-usa_1.0",
-        "title": "Elk and bison carcasses in Yellowstone, USA",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814899-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814899-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/elk-and-bison-carcasses-in-yellowstone-usa_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch, A.C., Frossard, A., Sch\u00fctz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology   doi: ...    Methods  Study area and study sites  This study was conducted in YNP\u2019s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163\u00b0 N, 110.4169\u00b0 W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.   For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).   The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter \u201cdays since kill\u201d, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP\u2019s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter \u201ccontrol\u201d) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter \u201ccarcass\u201d). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).     Ecosystem functions and soil properties  We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 \u00baC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5\u2009g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3\u2013V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).  Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.   Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, L\u00fcscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).  The other half of the soil sample was bagged in paper, dried to constant weight at 60\u00b0C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60\u00b0C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 \u2013 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.  In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18\u2019\u2019, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20\u00b0C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (\uf05bCTScontrol - CTSfinal\uf05d/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.    Statistical analyses  Univariate analyses for ecosystem functions, soil biotic and abiotic properties  We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (\u201cLocation\u201d), bison and elk (\u201cSpecies\u201d) and days since kill (\u201cDSK\u201d). For this purpose, we used linear mixed effect models (LMM, \u201cnlme\u201d package v 3.1 \u2013 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the \u201clsmeans\u201d package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.  We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran\u2019s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the \u201cmoran.test\u201d function in the \u201cspdep\u201d package version 1.1-3 (Bivand et al., 2019).   Multivariate analyses  Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray\u2013Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (\u201cvegan\u201d package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (\u201cnlme\u201d package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the \u201clsmeans\u201d package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the \u201cenvfit\u201d function in the \u201cvegan\u201d package (Oksanen et al., 2019).   Indicator species analyses were performed using the multipatt function implemented in the \u201cindicspecies\u201d package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De C\u00e1ceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).    References  Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., \u2026 K\u00f5ljalg, U. (2010). The UNITE database for molecular identification of fungi \u2013 recent updates and future perspectives. New Phytologist, 186(2), 281\u2013285. doi:10.1111/j.1469-8137.2009.03160.x  Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403\u20131414. doi:10.1111/1755-0998.12399  Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., \u2026 Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914\u2013919. doi:10.1111/2041-210X.12073  Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., \u2026 Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.  Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159\u20133167.  Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996\u20131007. doi:10.1007/s10021-009-9274-0  Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.  De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.  De C\u00e1ceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674\u20131684. doi:10.1111/j.1600-0706.2010.18334.x  Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.  Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476\u20133482. doi:10.1093/bioinformatics/btv401  Frey, B., Niklaus, P. A., Kremer, J., L\u00fcscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060\u20136068. doi:10.1128/AEM.05206-11  Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.  Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., B\u00e5\u00e5th, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191\u2013199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028  Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.  Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155\u2013e155. doi:10.1093/nar/gkq545  Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145\u2013155.  Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7\u20139). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.  Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.  Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10\u201312.  Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.  Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Bia\u0142owie\u017ca Primeval Forest, Poland. Ecological Research, 22(5), 807\u2013813. doi:10.1007/s11284-006-0321-4  Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7  Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., \u2026 Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.  Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.  Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., \u2026 Gl\u00f6ckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590\u2013D596. doi:10.1093/nar/gks1219  Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57\u201362.  R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.  R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.  Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091\u20131108. doi:10.1111/mec.13051  Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1\u20132), 191\u2013201. doi:10.1007/s11104-007-9354-x  Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., \u2026 Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537\u20137541. doi:10.1128/AEM.01541-09  Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., \u2026 Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.  Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261\u20135267. doi:10.1128/AEM.00062-07  Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68\u201375.      Please cite this paper together with the citation for the datafile.",
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-    },
     {
         "id": "em_database_1",
         "title": "Electron Microscope Database",
@@ -208038,19 +203709,6 @@
         "description": "This database contains information pertaining to the negatives taken by the laboratory since its inception.  Both scanning and transmission electron micrographs are catalogued within this database.  Among other things, the database includes a large number of images of protists.  The URLs provided link to a marine specimens database, and a terrestrial and limnetic specimens database.",
         "license": "proprietary"
     },
-    {
-        "id": "emergence-dynamics-of-natural-enemies_1.0",
-        "title": "Emergence dynamics of natural enemies of spruce bark beetles",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.2987976, 47.0713378, 8.9689636, 47.3846826",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814953-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814953-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/emergence-dynamics-of-natural-enemies_1.0",
-        "description": "In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.",
-        "license": "proprietary"
-    },
     {
         "id": "enderby_flight_logs_1977_1",
         "title": "Enderby Land Flight Logs For Ice Radar and Navigation, 1977",
@@ -208116,604 +203774,6 @@
         "description": "In 1977/78 ANARE carried out a summer operation that was a continuation of a multi-year project in the Enderby Land region, commenced in the 1974/75 season. Programs for 1977/78 included survey, high level photography, geochronolgy, structural geology, petrology, geophysics and glaciology. The programs were air supported from a field base near Mt. King (67 degrees 04'S, 52 degrees 52'E).  Planning and daily logbooks for the program, as well as the end of season report, have been archived at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "endsplit_1.0",
-        "title": "EndSplit",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-71.7920494, 43.9260264, -71.685276, 43.9638472",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814993-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789814993-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/endsplit_1.0",
-        "description": "R scripts and demonstration data for end-member mixing and splitting: using isotopes and other tracers to determine where streamflow comes from (end-member mixing) and where precipitation goes (end-member splitting).      # This package includes two R scripts: #    \"EndSplit_v1.0_20200516.R\" implements end-member mixing and splitting.   \"EndSplit_demo_v1.0_20200516.R\" demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set (see below).    Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/    But\u2026 READ THIS CAREFULLY:  ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.    These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, \"Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis\", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.    Build 2020.05.16 is a minor bug fix of build 2019.10.25, which was previously released as EndSplit_v1.0_20191025.R.    # The zip file \"demonstration input data.zip\" contains 8 demonstration data files (all tab-delimited text): #    \"Hubbard Brook WS3 isotope data split by sampling date.txt\" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).  \"Hubbard Brook WS3 daily P and Q 1956-2014.txt\" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).    \"Hubbard Brook WS3 isotope data WY2007.txt\", \"Hubbard Brook WS3 isotope data WY2008.txt\", \"Hubbard Brook WS3 isotope data WY2009.txt\", \"Hubbard Brook WS3 daily P and Q WY2007.txt\", \"Hubbard Brook WS3 daily P and Q WY2008.txt\", and \"Hubbard Brook WS3 daily P and Q WY2009.txt\" contain subsets of these data for the designated water years.      As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:    Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.    USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.    USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.      # The zip file \"demonstration output files.zip\" contains demonstration output files  #    These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in \"demonstration input data.zip\"",
-        "license": "proprietary"
-    },
-    {
-        "id": "energy-cooperatives-in-switzerland-survey-results_1.0",
-        "title": "Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082125-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082125-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/energy-cooperatives-in-switzerland-survey-results_1.0",
-        "description": "## Topic of Survey  The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project \"Collective financing of renewable energy projects in Switzerland and Germany\" of the National Research Programme 71 \"Managing Energy Consumption\". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.    ## Survey Method  The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software \"Sawtooth\". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by \"missing data\".     # Anonymization  Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.    # How to Use the Data  * The data are available in CSV and SPSS (sav.) format.  * A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.     # Current Embargo on Data  These data are currently under embargo and will only be released when the project is completed (not before 2020).    #Additional Information  * The used questionnaire is provided in German and French.   * Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943",
-        "license": "proprietary"
-    },
-    {
-        "id": "ensemble-hydrograph-separation_1.4",
-        "title": "Ensemble hydrograph separation scripts",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815032-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815032-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/ensemble-hydrograph-separation_1.4",
-        "description": "Calculation scripts that perform ensemble hydrograph separation.  Identical scripts in R and MATLAB are provided, along with demonstration input time series and the corresponding outputs.  These scripts were tested on R version 3.6.2 (2019-12-12) and on MATLAB versions 2018b and 2109b.      These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/.    ETH Zurich, WSL, James Kirchner, and Julia Knapp make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-12_2019-03-06",
-        "title": "Meteorological measurements LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 45.86141, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815107-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815107-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/envidat-lwf-12_2019-03-06",
-        "description": "Continuous measurement of air temperature, relative humidity, wind speed and direction, global radiation, photosynthetic active radiation, UVB radiation, and precipation in an open field very close to the LWF plot as well as air temperature, relative humidity, wind speed, photosynthetic active radiation, and precipitation in the forest below the canopy.    ### Purpose: ###    Recording meteorological conditions        ### Manual Citation: ###     * Martine Rebetez, Gustav Schneiter, 1997: Meteorologie. In: Brang P. (ed.) Aufnahmeanleitung LWF. Langfristige Wald\u00f6kosystem-Forschung LWF, 4 S.      *  Raspe  S,  Beuker  E,  Preuhsler  T,  Bastrup-Birk  A,  2016:  Part  IX:  Meteorological  Measurements.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  14  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Martine Rebetez,  Georg von Arx, Arthur Gessler, Elisabeth Graf Pannatier, John L. Innes, Peter Jakob, Mark\u00e9ta Jetel, Marlen Kube, Magdalena N\u00f6tzli, Marcus Schaub, Maria Schmitt, Flurin Sutter, Anne Thimonier, Peter Waldner, Matthias Haeni, 2018: Meteorological data series from Swiss long-term forest ecosystem research plots since 1997. Annals of Forests Science 75: 41: 1-7. [doi: 10.1007/s13595-018-0709-7](https://doi.org/10.1007/s13595-018-0709-7)      *  Haeni, Matthias; von Arx, Georg; Gessler, Arthur; Graf Pannatier, Elisabeth; Innes, John L; Jakob, Peter; Jetel, Mark\u00e9ta; Kube, Marlen; N\u00f6tzli, Magdalena; Schaub, Marcus; Schmitt, Maria; Sutter, Flurin; Thimonier, Anne; Waldner, Peter; Rebetez, Martine (2016): Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF) in Switzerland, from 1996-2016. PANGAEA, [doi: 10.1594/PANGAEA.868390](https://doi.org/10.1594/PANGAEA.868390)      *  Gustav Schneiter, Peter Jakob, Martine Rebetez, 2004: Sieben Jahre meteorologische Datenerfassung im Schweizer Wald. Infoblatt Forschungsbereich Wald, Vol 17: 4-6 [>>>](https://www.parcs.ch/snp/pdf_public/2011_schneiteretal_datenerf_wald_wsl_2004.pdf)      *  Jakob P, Sutter F, Waldner P, Schneiter G (2007) Processing remote gauging-data. In: Gomez J. M., Sonnenschein M., M\u00fcller M., Welsch H., Rautenstrauch C. (ed.) Information Technologies in Environmental Engineering ITEE 2007, Third International ICSC Symposium, Springer, Berlin, Heidelberg, 211-220.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-15_2019-03-06",
-        "title": "Atmospheric deposition (throughfall and bulk deposition) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815119-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815119-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/envidat-lwf-15_2019-03-06",
-        "description": "Throughfall (precipitation under forest canopy) is a major pathway in forest nutrient cycling, and its quantification is necessary to establish both water and nutrient budgets. Furthermore, parallel sampling of throughfall and precipitation in the open field (bulk precipitation), together with assumptions about the canopy exchange processes (uptake and leaching of nutrients), allow the atmospheric deposition of nutrients and pollutants to be quantifed. Bulk precipitation and throughfall have been sampled since 1994 or later on 15 LWF plots using 3 (in the open) and 16 (in the forest) funnel-type precipitation collectors. These collectors are replaced by 1 (open area) and 4 (forest stand) snow buckets in winter on plots with abundant precipitation in the form of snow. The length of sampling intervals is usually 14 days.    ### Purpose: ###    To assess a major flux of the water and nutrient budget in forests, and to quantify the atmospheric deposition of nitrogen, sulphur and other nutrients. Atmospheric deposition is one of the key factors in the causal chain between emission of air pollutants and acidifying or eutrophying effects in forest ecosystems.        ### Manual Citation: ###     * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosph\u00e4rische Deposition: Freiland- und Bestandesniederschl\u00e4ge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.      *  Clarke  N,  \u017dlindra  D,  Ulrich  E,  Mosello  R,  Derome  J,  Derome  K,  K\u00f6nig  N,  L\u00f6vblad  G,  Draaijers  GPJ,  Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling,  assessment,  monitoring  and  analysis  of the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    32    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Schmitt M, Waldner P, Rihm B (2005) Atmospheric deposition on Swiss Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and Assessment, 104: 81-118. [doi: 10.1007/s10661-005-1605-9](http://doi.org/10.1007/s10661-005-1605-9)      *  Thimonier A, Kosonen Z, Braun S, Rihm B, Schleppi P, Schmitt M, Seitler E, Waldner P, Th\u00f6ni L (2019) Total deposition of nitrogen in Swiss forests: Comparison of assessment methods and evaluation of changes over two decades. Atmospheric Environment, 198: 335-350. [doi: 10.1016/j.atmosenv.2018.10.051](http://doi.org/10.1016/j.atmosenv.2018.10.051)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-16_2019-03-06",
-        "title": "Stemflow LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.02261, 9.06707, 47.47836",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815134-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815134-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/envidat-lwf-16_2019-03-06",
-        "description": "Stemflow (portion of precipitation running down the branches and the trunk and depositing at the base of the tree) can represent a substantial fraction of the total water and nutrient input in stands of smoothbarked species with upright branches. Stemflow was measured with silicone gutters installed on the trunk of 5 trees at three LWF plots during 1-2 years. High capacity containers were used at Novaggio. An automated tipping bucket system, allowing continuous recording of volumes and sampling of representative proportional fraction, is currentlx used at the LWF sites Laegeren, Lausanne, Othmarsingen and Sch\u00e4nis.     ### Purpose: ###    To quantify the contribution of stemflow to the water and nutrient budget and to the atmospheric deposition in selected forests stands.        ### Manual Citation: ###     * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosph\u00e4rische Deposition: Freiland- und Bestandesniederschl\u00e4ge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.      *  Clarke  N,  \u017dlindra  D,  Ulrich  E,  Mosello  R,  Derome  J,  Derome  K,  K\u00f6nig  N,  L\u00f6vblad  G,  Draaijers  GPJ,  Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling,  assessment,  monitoring  and  analysis  of the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    32    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-17_2019-03-06",
-        "title": "Litterfall LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815151-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815151-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/envidat-lwf-17_2019-03-06",
-        "description": "Litterfall is a key parameter in the biogeochemical cycle of forest ecosystems, linking the tree part to the soil compartment. Litterfall has been collected on 15 LWF plots using 10 traps that are emptied every 4 to 8 weeks since 1996 or later. Both the biomass of the litter and its chemical content (including heavy metals) are measured, in order to quantify the annual return of nutrients and organic matter to the soil. Furthermore, the analysis of the temporal pattern of litterfall production gives insight into possible effects of anthropogenic and natural factors (e.g. severe drought) on the ecosystem and the vitality of the forest stand, provides information on the phenological development of the stand, and, in particular, allows mast years to be identified. At 7 broadleaved sites, litterfall was also used to estimate the leaf area index (LAI) of the forest stand.     ### Purpose: ###    To quantify the annual return of nutrients and organic matter to the soil.         ### Manual Citation: ###     * Thimonier, A., Brang, P., Ottiger, A., 1997. Kapitel C5. Streufall, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-18.      *  Ukonmaanaho  L.,  Pitman  R,  Bastrup-Birk  A,  Breda  N,  Rautio  P,  2016:  Part  XIII:  Sampling  and  Analysis  of  Litterfall.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute for Forests Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [doi: 10.1007/s10342-009-0353-8](http://doi.org/10.1007/s10342-009-0353-8 )",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-18_2019-03-06",
-        "title": "Foliar analyses LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815171-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815171-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/envidat-lwf-18_2019-03-06",
-        "description": "Foliage has been sampled every two years since 1995/1997 on 5-6 trees of the main  species on all LWF plots. Concentrations of macronutrients (N, P, K, Ca, Mg, S), carbon (C) and micronutrients are determined on leaves and current and previous year needles. The dry mass of 100 leaves or 1000 needles is determined as well.    ### Purpose: ###    To assess the nutrient status of the forest stands and detect possible deficiencies or imbalances, which are often indicative of processes at the ecosystem level.        ### Manual Citation: ###     * Brang, P., Hug, C., Thimonier, A., Zehnder, U., 1997. Kapitel D1.5 N\u00e4hrstoffversorgung von Nadeln und Bl\u00e4ttern, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-12.      *  Rautio P, F\u00fcrst A, Stefan K, Raitio H, Bartels U, 2016: Part XII: Sampling and Analysis of Needles and Leaves.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on   forests.   Th\u00fcnen   Institute   of   Forest   Ecosystems,   Eberswalde,   Germany,   19   p.   +   Annex   [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Graf Pannatier E, Schmitt M, Waldner P, Walthert L, Schleppi P, Dobbertin M, Kr\u00e4uchi N (2010) Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites?. European Journal of Forest Research, 129 (3): 443-461. [doi: 10.1007/s10342-009-0328-9](http://doi.org/10.1007/s10342-009-0328-9)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-19_2019-03-06",
-        "title": "Circular vegetation plots LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815198-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815198-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/envidat-lwf-19_2019-03-06",
-        "description": "Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relev\u00e9s were repeatedly carried out at 17 LWF plots in the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). Phytosociological relev\u00e9s were carried out in one or two concentric circular plots of 30, 200, 400 and 500 m2. All species occurring on the whole area of the LWF plot were also noted during the first vegetation survey.    ### Purpose: ###    To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value.        ### Manual Citation: ###     * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9.      * Canullo  R,  Starlinger  F,  Granke  O,  Fischer  R,  Aamlid  D,  2016:  Part  VI.1:  Assessment  of  Ground  Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  12  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-20_2019-03-06",
-        "title": "Permanent vegetation quadrats LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815218-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815218-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/envidat-lwf-20_2019-03-06",
-        "description": "Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relev\u00e9s were carried out repeatedly at 17 LWF plots during in the the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). The cover of all plant species occurring in 16 1-m2 quadrats, distributed over the 43 x 43 m intensive monitoring subplot was visually assessed. Seedlings and saplings were also counted and their position within the quadrat was noted in order to assess tree regeneration.    ### Purpose: ###    To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value.        ### Manual Citation: ###     * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9.      * Canullo  R,  Starlinger  F,  Granke  O,  Fischer  R,  Aamlid  D,  2016:  Part  VI.1:  Assessment  of  Ground  Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  12  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-21_2019-03-06",
-        "title": "Leaf area index (LAI) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815247-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815247-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lwf-21_2019-03-06",
-        "description": "Leaf area index (LAI), defined as the total one-sided foliage area per unit ground surface area, is one of the most important characteristics of plant canopy structure. Leaves are the active interface between the atmosphere and the ecosystem. Thus, LAI affects many ecosystem processes, including light and precipitation interception, evapotranspiration, CO2 fluxes and dry deposition. LAI was measured repeatedly in the period 1996-2013 at 18 LWF plots using 1) a LAI-2000 plant canopy analyser (Licor, Inc) and 2) hemispherical photographs of the canopy. Measurements were performed above the 16 vegetation quadrats in the 43 m x 43 m intensive monitoring subplot. In 1996-2003, LAI measurements were usually carried out on the same day as the vegetation surveys. It is also planned to characterise the potential light conditions (diffuse and direct) using the hemispherical photographs of the canopy.    ### Purpose: ###    1) To estimate an important structural parameter of the forest stand, which is needed as an input variable in most ecosystem process models simulating carbon and water cycles on a stand or regional scale; and 2) to document changes in the canopy structure, and thus in light conditions, which may be responsible for changes in ground vegetation        ### Manual Citation: ###     * Thimonier, A., 1997. Kapitel C6. Blattfl\u00e4chenindex (LAI), in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-5.      *  Thimonier, A., 1997. Kapitel C7. Lichtverh\u00e4ltnisse im Wald, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Fl\u00e4chen der Langfristigen Wald\u00f6kosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-4.      *  Fleck  S,  Raspe  S,  Cater  M,  Schleppi  P,  Ukonmaanaho  L,  Greve  M,  Hertel  C,  Weis  W,  Rumpf,  S.,  Thimonier, A., Chianucci, F., Becksch\u00e4fer, P., 2016: Part XVII: Leaf Area Measurements. In: UNECE ICP Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    34    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](https://doi.org/10.1007/s10661-010-1759-y)      *  Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [10.1007/s10342-009-0353-8](https://doi.org/10.1007/s10342-009-0353-8)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-22_2019-03-06",
-        "title": "Passive sampling of NH3 LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29, 46.02, 10.23, 47.48",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815262-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815262-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/envidat-lwf-22_2019-03-06",
-        "description": "NH&#8323; concentrations were measured at 11 LWF plots (1999/2000) with Z\u00fcrcher passive samplers (Palmes-type diffusion tubes with an acidic solution as absorbent) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NH&#8323; concentrations were measured again at 14 plots, using two Radiello samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.     ### Purpose: ###    To assess air concentrations of ammonia (NH&#8323;) and, using deposition velocities available from the literature, to quantify the dry deposition of NH&#8323; (alternative method to the throughfall method). The LWF plots were part of a larger network covering the main regions of Switzerland. One objective of this larger network was to compare measured and modelled concentrations.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-23_2019-03-06",
-        "title": "Passive sampling of NO2 LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29, 46.02, 10.23, 47.48",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815276-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815276-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/envidat-lwf-23_2019-03-06",
-        "description": "NO&#8322; concentrations were measured at 11 LWF plots (1999/2000) with passive samplers (Palmes-type diffusion tubes) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NO2 concentrations were measured again at 14 plots, using two samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.     ### Purpose: ###    To assess air concentrations of nitrogen dioxide (NO2) and, using deposition velocities available from the literature, to quantify the dry deposition of NO2 (alternative method to the throughfall method).",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-24_2019-03-06",
-        "title": "Phenological observations LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.47836",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815288-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815288-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/envidat-lwf-24_2019-03-06",
-        "description": "Phenological observations are recorded every 14 days on LWF plots where throughfall and bulk precipitation are sampled. The percentage of foliage in reference to its maximum potential development in summer, the percentage of foliage with autumnal discoloration and the percentage of fallen leaves (broadleaved stands) are estimated at the plot level. At two LWF plots (Othmarsingen, Vordemwald), phenological stages are documented on individual trees     ### Purpose: ###    To document the seasonal development of the canopy of trees and shrubs at the plot level",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-25_2019-03-06",
-        "title": "Soil morphology LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815298-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815298-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lwf-25_2019-03-06",
-        "description": "Description of several morphological soil properties at the beginning of the monitoring campaign. The properties were described for all genetic horizons in soil pits if possible down to the parent material. In heterogeneous LWF-plots, several soil profiles were described in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.    ### Purpose: ###    Morphological soil properties are important for the calculation or interpretation of chemical or physical soil properties or processes. For instance, root distribution is an important input-parameter of water balance models or soil hydromorphy strongly affects the chemical status of soil matrix and soil solution.        ### Manual Citation: ###     * Walthert L, L\u00fcscher P, Luster J, Peter B (2002) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)        ### Paper Citation: ###     * Walthert L, Blaser P, L\u00fcscher P, Luster J, Zimmermann S (2003) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-26_2019-03-06",
-        "title": "Soil matrix chemistry LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815029-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815029-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lwf-26_2019-03-06",
-        "description": "Assessment of several chemical soil parameters at the beginning of the monitoring campaign. Most parameters were determined accordung to the manual of ICP-Integrated-Monitoring. The parameters were analysed for all genetic horizons in soil pits and additionally for fixed layers in the Intensive-Monitoring-Plots. In heterogeneous LWF-plots, several soil pits were analysed in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.    ### Purpose: ###    The chemical characterisation of soil matrix down to the paraent material is realised with data from soil pits. The monitoring of the soil matrix in a frequency of roundly 15 years is effected with soil samples from Intensiv-Monitoring-Plots. For soil monitoring, pooled samples with 16 replicats are used down to a depth of 80 cm. The date of the second soil survey is not yet fixed.        ### Manual Citation: ###     * Walthert L, L\u00fcscher P, Luster J, Peter B (2002) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)        ### Paper Citation: ###     * Walthert L, Blaser P, L\u00fcscher P, Luster J, Zimmermann S (2003) Langfristige Wald\u00f6kosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Z\u00fcrich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-27_2019-03-06",
-        "title": "Matric potential (manual suction cups) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815062-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815062-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/envidat-lwf-27_2019-03-06",
-        "description": "Measurement of the soil water availability to plants at 10 LWF plots  every 14 days in 5 soil depths (15, 30, 50, 80, 130 cm) with 8 replicates (usually in the IM plot). The range of measurement is from water saturation until-80 kPa.    ### Purpose: ###    The long-term measurement of the soil water availability to plants in the root zone provides useful information about the soil moisture conditions (drought, water saturation, water easily available to plants). The measurement of the soil water suction allows to calibrate the water balance models and to validate the modelled matric potential.        ### Manual Citation: ###     * Peter Waldisp\u00fchl, 1997: Installation von Tensiometern auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung LWF, Birmensdorf, 2 S.      *  Peter Waldisp\u00fchl, Andreas Rigling, 1997: Vorgehen bei der Ablesung von Teniometern auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 2 S.      *  Peter Waldisp\u00fchl, 2000: Kurzanleitung f\u00fcr die TensioDB. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 12 S. + DB-Schema        ### Paper Citation: ###     * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-28_2019-03-06",
-        "title": "Soil solution chemistry (lysimeters) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.02261, 9.8888, 47.47836",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815111-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815111-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lwf-28_2019-03-06",
-        "description": "Fortnightly measurement of the soil solution chemistry in 4 soil depths  with zero-tension lysimeter below the litter layer and with tension lysimeters at depths of 15, 50 and 80 cm (8 replicates)     ### Purpose: ###    To characterize the chemical status of the soil solution and to detect trends in soil water quality. To assess the effects of air pollution and climate chnage on soil water quality.        ### Manual Citation: ###     * Micha Pluess, Daniel Christen, 1999: Kurzanleitung Bodenl\u00f6sung. Langfristige Wald\u00f6kosystem-Forschung LWF, Birmensdorf, 2 S.       * Nieminen  TM,  De  Vos  B,  Cools  N,  K\u00f6nig  N,  Fischer  R,  Iost  S,  Meesenburg  H,  Nicolas  M,  O\u2019Dea  P,  Cecchini  G,  Ferretti  M,  De  La  Cruz  A,  Derome  K,  Lindroos  AJ,  Graf  Pannatier  E,  2016:  Part  XI:  Soil  Solution  Collection  and  Analysis.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Th\u00fcnen Institute of Forest Ecosystems, Eberswalde, Germany, 20 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-29_2019-03-06",
-        "title": "TDR soil water content measurements LWF Visp",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.85832, 46.29688, 7.85832, 46.29688",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815123-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815123-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/envidat-lwf-29_2019-03-06",
-        "description": "Continuous measurement of soil water content at 15, 50 and 70 cm depth in Visp (3 replications) with TDR soil moisture probes (Tektronix 1502 B) from 2001 until 25.04.2013    ### Purpose: ###    Improve the available data for the calibration or validation of the water balance models, i.e. the determination of the water flux needed for calculating the leaching fluxes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-30_2019-03-06",
-        "title": "EC-5 soil water content measurement LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.02261, 9.8888, 47.39954",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815137-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815137-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/envidat-lwf-30_2019-03-06",
-        "description": "Continuous measurement of soil water content at 15, 50 and 80 cm depth (3 replications) with ECH2O EC-5 soil moisture probes (Decagon Incorporation, Pullman, WA, USA).      ### Purpose: ###    Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-31_2019-03-06",
-        "title": "MPS-2 soil water matric potential LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.85832, 46.29688, 7.85832, 46.29688",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815159-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815159-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/envidat-lwf-31_2019-03-06",
-        "description": "Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors    ### Purpose: ###    Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-32_2019-03-06",
-        "title": "MPS-2 on LWF Visp to survey 2017 mortality wave",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.85832, 46.29688, 7.85832, 46.29688",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815187-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815187-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/envidat-lwf-32_2019-03-06",
-        "description": "Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors  1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs    ### Purpose: ###    Explore the effect of shrubs on the water availability for pine trees in Visp.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-33_2019-03-06",
-        "title": "TDR Pfynwald",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.61211, 46.30279, 7.61211, 46.30279",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815214-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815214-ENVIDAT.html",
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-        "description": "Continuous measurement of soil water content at one control and    in one irrigated plot in 10, 40 and 60 cm depth (4 replications) with TDR (Tektronix    1502B cable tester, Beaverton, OR, US).    ### Purpose: ###    Monitoring of the soil water content        ### Paper Citation: ###     * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)",
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-        "id": "envidat-lwf-34_2019-03-06",
-        "title": "10-HS Pfynwald",
-        "catalog": "ENVIDAT STAC Catalog",
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-        "end_date": "2019-01-01",
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-        "description": "Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA).    ### Purpose: ###    Monitoring of the soil water matrix potential        ### Paper Citation: ###     * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)",
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-        "id": "envidat-lwf-36_2019-03-06",
-        "title": "Passive sampling of O3 LWF",
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-        "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment: Measurements of mean ozone concentrations with passive samplers (passam ag).        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G,  L\u00f6vblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  XV:  Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  11  p.  +  Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth \u2013 towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](http://doi.org/10.1111/1365-2745.12941)      *  Calatayud V, Di\u00e9guez JJ, Sicard P, Schaub M, De Marco A (2016) Testing approaches for calculating stomatal ozone fluxes from passive sampler. Science of the Total Environment. [doi:10.1016/j.scitotenv.2016.07.155](http://doi.org/10.1016/j.scitotenv.2016.07.155)      *  Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](http://doi.org/10.1016/B978-0-08-098222-9.00019-4)",
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-    {
-        "id": "envidat-lwf-37_2019-03-06",
-        "title": "Continuous measurement of O3 LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815274-ENVIDAT.umm_json",
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-        "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment: Continuous measurements of ozone concentrations        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G,  L\u00f6vblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  XV:  Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  11  p.  +  Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth \u2013 towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](https://doi.org/10.1111/1365-2745.12941)      *  Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](https://doi.org/10.1016/B978-0-08-098222-9.00019-4)",
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-    },
-    {
-        "id": "envidat-lwf-38_2019-03-06",
-        "title": "Symptoms of O3 injuries LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815286-ENVIDAT.umm_json",
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-        "description": "Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G, L\u00f6vblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  VIII:  Monitoring  of  Ozone  Injury.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, H\u00e4ni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu\u00a8nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X)",
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-    {
-        "id": "envidat-lwf-45_2019-03-06",
-        "title": "Tree Diameter and Height Inventory LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815297-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815297-ENVIDAT.html",
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-        "description": "Tree circumference, height, height to crown base measurements, mortality, decay class and removal assessment on LWF plots    ### Purpose: ###    Assessment of tree and forest growth        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, 1999: Vorl\u00e4ufige Feld-Aufnahmeanleitung f\u00fcr die BHU- und H\u00f6hen-Inventur auf LWF-Fl\u00e4chen (V1.0), Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 18 S.      *  Christian Hug, Matthias Dobbertin, Chris Nussbaumer, Yves Stettler, 2010: Provisorische Aufnahmeanleitung f\u00fcr die Brusth\u00f6henumfang- und H\u00f6heninventur auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 29 S.      *  Christian Hug, Chris Nussbaumer, Yves Stettler, 2014: Aufnahmeanleitung f\u00fcr die Brusth\u00f6henumfang und H\u00f6heninventur auf LWF-Fl\u00e4chen. Langfristige Wald\u00f6kosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 36 S.      *  Dobbertin  M,  Neumann  M,  2016:  Part  V:  Tree  Growth.  In:  UNECE  ICP  Forests,  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://dx.doi.org/10.1016/j.foreco.2013.05.040)",
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-    {
-        "id": "envidat-lwf-47_2019-03-06",
-        "title": "Crown Condition Assessment and Damage Cause Assessment Sanasilva",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815309-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815309-ENVIDAT.html",
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-        "description": "Annual Crown Condition Assessment including mortality and removal and Damage Causse Assessment on the Sanasilva-Sites and LWF plots.    ### Purpose: ###    To assess tree and forest health and its changes and to assess occurence and extent of diseases        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Fl\u00e4chen (Version 10). Sanasilva Inventur und Langfristige Wald\u00f6kosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)      *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schr\u00f6ck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )        ### Paper Citation: ###     * BAFU (2017) Jahrbuch Wald und Holz 2017. Umwelt-Zustand, Bundesamt f\u00fcr Umwelt, Bern, Vol. 1718: 110 p. [>>>](http://www.bafu.admin.ch/uz-1718-d)     *  Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [Technical Reports](http://icp-forests.net/page/icp-forests-technical-report)      *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gef\u00e4hrdung des Schweizer Waldes \u2013 eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt f\u00fcr Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)",
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-    },
-    {
-        "id": "envidat-lwf-48_2019-03-06",
-        "title": "Crown Condition Assessment and Damage Cause Assessment LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815341-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815341-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiY29tbXVuaXR5IHN0cnVjdHVyZSwgbGlmZS1oaXN0b3J5IHRyYWl0cyBhbmQgcGVyZm9ybWFuY2UgdHJhaXRzIG9mIHVyYmFuIGNhdml0eS1uZXN0aW5nIGJlZXMgYW5uZCB3YXNwc1wiLFwiRU5WSURBVFwiLFwiY29tbXVuaXR5LXN0cnVjdHVyZS1saWZlLWhpc3RvcnktdHJhaXRzLWFuZC1wZXJmb3JtYW5jZS10cmFpdHMtb2YtdXJiYW4tY25id1wiLFwiMS4wXCIsMzIyNjA4MTgxNSwyXSIsInVtbSI6IltcImNvbW11bml0eSBzdHJ1Y3R1cmUsIGxpZmUtaGlzdG9yeSB0cmFpdHMgYW5kIHBlcmZvcm1hbmNlIHRyYWl0cyBvZiB1cmJhbiBjYXZpdHktbmVzdGluZyBiZWVzIGFubmQgd2FzcHNcIixcIkVOVklEQVRcIixcImNvbW11bml0eS1zdHJ1Y3R1cmUtbGlmZS1oaXN0b3J5LXRyYWl0cy1hbmQtcGVyZm9ybWFuY2UtdHJhaXRzLW9mLXVyYmFuLWNuYndcIixcIjEuMFwiLDMyMjYwODE4MTUsMl0ifQ%3D%3D/envidat-lwf-48_2019-03-06",
-        "description": "Assessment of damges, i.e. symptoms, extent and causes on trees.    ### Purpose: ###    Occurrence and extent of diseases        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Fl\u00e4chen (Version 10). Sanasilva Inventur und Langfristige Wald\u00f6kosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)      *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schr\u00f6ck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )        ### Paper Citation: ###     * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [>>>](http://icp-forests.net/page/icp-forests-technical-report)      *  K\u00f6hl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [>>>](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf)     *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gef\u00e4hrdung des Schweizer Waldes \u2013 eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt f\u00fcr Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-49_2019-03-06",
-        "title": "Deadwood survey LWF 1995 - line intersect",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815039-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815039-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-49_2019-03-06",
-        "description": "Assessment of coarse woody debris on LWF plots using the line intersect method     ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.        ### Paper Citation: ###     * Bretz Guby, N.A., Dobbertin, M., 1996. Quantitative estimates of coarse woody debris and standing dead trees in selected Swiss forests. Glob. Ecol. Biogeogr. Lett. 5, 327-341.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-50_2019-03-06",
-        "title": "Deadwood survey LWF 2005 - subplot (Forets Biota)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815071-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815071-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-50_2019-03-06",
-        "description": "Assessment of coarse woody debris on LWF plots using full count methods on defined subplots (applying ForestBiota protocoll)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Franziska Heinrich, 2005: Totholz-Aufnahme mit ForestBIOTA Protokoll. Langfristige Wald\u00f6kosystem-Forschung LWF. Eidg. Forschungsanstalt WSL, Birmensdorf, 7 S.      *  ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)      *  Fischer, R., Fischer, R., Seidling, W., Granke, O., Meyer, P., Stofer, S., Travaglini, D., 2007. ForestBIOTA \u2013 Testphase zur Erfassung der biologischen Vielfalt. AFZ/Wald 62, 1070.        ### Paper Citation: ###     * Seidling, W., Travaglini, D., Meyer, P., Waldner, P., Fischer, R., Granke, O., Chirici, G., Corona, P., 2014. Dead wood and stand structure - relationships for forest plots across Europe. IForest - Biogeosciences and Forestry 7, 269-281.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-51_2019-03-06",
-        "title": "Deadwood survey LWF 2013 - subplot",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815114-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815114-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-51_2019-03-06",
-        "description": "Assessment of coarse woody debris on LWF plots using the line intersect method and full count methods on subplots (repetition of the 2005 survey)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-52_2019-03-06",
-        "title": "Deadwood survey LWF 2013 - line intersect",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815126-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815126-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-52_2019-03-06",
-        "description": "Assessment of coarse woody debris on LWF plots using the line intersect method (repetition of the 1995 survey)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-53_2019-03-06",
-        "title": "Deadwood survey Sanasilva 2013",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815144-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815144-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-53_2019-03-06",
-        "description": "Assessment of coarse woody debris on Sanasilva plots (16x16 km grid) using the full count methods on subplots applying BioSoil protocoll    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * A. Bastrup-Birk, P. Nevile, G. Chirici, T. Houston, 2006: The BioSoil ForestBiodiversity Field Manual Version 1.0/1.1/1.1A for the Field Assessment 2006-07. Working Group on ForestBiodiversity, Forest Focus Demonstration Project BioSoil 2004-2005, 47 S.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-54_2019-03-06",
-        "title": "Sapflow measurements LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.41665, 46.02261, 9.85521, 47.22516",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815161-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815161-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/envidat-lwf-54_2019-03-06",
-        "description": "Continuous sap flow measurements with Granier-needles to investigate carbon balance and water relations of trees    ### Purpose: ###    Assessment of water cycle processes",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-56_2019-03-06",
-        "title": "Manual circumference band measurement LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815183-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815183-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/envidat-lwf-56_2019-03-06",
-        "description": "Tree circumference change measurements from plastic girth bands    ### Purpose: ###    Assessment of annual tree stem growth         ### Manual Citation: ###     * Dobbertin  M,  Neumann  M,  2016:  Part  V:  Tree  Growth.  In:  UNECE  ICP  Forests,  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Th\u00fcnen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://doi.org/10.1016/j.foreco.2013.05.040)",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-57_2019-03-06",
-        "title": "Automated point dendrometer measurements at LWF sites",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.83416, 46.02261, 9.85521, 46.81535",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815203-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815203-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/envidat-lwf-57_2019-03-06",
-        "description": "Continuous stem radius measurements to investigate carbon balance and water relations of trees    ### Purpose: ###    Assessment of growth and water related stem changes",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-81_2019-03-06",
-        "title": "Dendrochronological analyses of tree core samples (dominant trees) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815223-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815223-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-81_2019-03-06",
-        "description": "Two stem core samples of 12 to 20 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 12 pre-dominant or dominant trees and if possible also 4 subdominant or surpressed trees. NOTE: The samplings were carried out between 1996 and 1999 for most plots and in 2003 for the plot Lantsch. The cores cover a time span depending on the age of the oldest trees on a plot. On one plot the oldest sampled tree ring grew in the year 1646.     ### Purpose: ###    Reconstruction of stand history and tree growth        ### Manual Citation: ###     * Paolo Cherubini, Matthias Dobbertin, 1997: Bestandesgeschichte (Dendrochronologie). In: Peter Brang (ed.), Aufnahmeanleitung LWF. Langfristige Wald\u00f6kosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 3 S.      *  Cherubini, P.; Dobbertin, M., 1998: The Swiss long-term forest ecosystem research: methods for reconstructing forest history. In: Borghetti, M. (ed): Societ\u00e0 Italiana di Selvicoltura ed Ecologia Forestale (SISEF), Atti I: 19-22.        ### Paper Citation: ###     * Cherubini, P., Fontana, G., Rigling, D., Dobbertin, M., Brang, P., Innes, J.L., 2002. Tree-life history prior to death: two fungal root pathogens affect tree-ring growth differently. J. Ecol. 90, 839-850.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-82_2019-03-06",
-        "title": "Dendrochronological analyses of tree core samples (CATS) adjacent to LWF sites",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.58377, 8.53568, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815251-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815251-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-82_2019-03-06",
-        "description": "There were 2 cores taken at 1.3 m height from each of 10 trees outside the LWF plot    ### Purpose: ###    influence of drought and nutrient availabiliy on tree growth        ### Paper Citation: ###     * L\u00e9vesque, M., Walthert, L., Weber, P., 2016. Soil nutrients influence growth response of temperate tree species to drought. J. Ecol. 104, 377-387.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-83_2019-03-06",
-        "title": "Dendrochronological analyses of tree core samples (IsoN) adjacent to LWF sites",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.58377, 8.71258, 47.39954",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815271-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815271-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-83_2019-03-06",
-        "description": "Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees.     ### Purpose: ###    N and C stable isotope signals        ### Paper Citation: ###     * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-84_2019-03-06",
-        "title": "Dendrochronological analyses of tree core samples (subplot) LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.88676, 46.81535, 9.85521, 47.47836",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815285-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815285-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-84_2019-03-06",
-        "description": "Two stem core samples of all trees of a subplot of approximately 1 a. Basal Area, Wood volume increment per area estimation    ### Purpose: ###    Investigation of the relation between dendroparameters of dominance/surpression and stem growth. Abgleich Jahrringdaten mit Inventurdaten.        ### Paper Citation: ###     * Nehrbass-Ahles C, Babst F, Klesse S, N\u00f6tzli M, Bouriaud O, Neukom R, Dobbertin M, Frank D (2014) The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology, 20 (9): 2867\u20132885. [doi: 10.1111/gcb.12599](http://doi.org/10.1111/gcb.12599)     *  Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-86_2019-03-06",
-        "title": "Deadwood sampling at Sanasilva and LWF sites",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29085, 46.02261, 10.23009, 47.6837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815296-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815296-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/envidat-lwf-86_2019-03-06",
-        "description": "Sampling of deadwood for density and chemical analysis during summer 2009    ### Purpose: ###    Determination of N and C pools of deadwood        ### Paper Citation: ###     * WEGGLER, K.; DOBBERTIN, M.; J\u00dcNGLING, E.; KAUFMANN, E.; TH\u00dcRIG, E., 2012. Dead wood volume to dead wood carbon: the issue of conversion factors. European Journal of Forest Research 131, 1423-1438.",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat-lwf-87_2019-03-06",
-        "title": "Stem discs LWF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.43594, 46.26856, 9.85521, 47.39954",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815308-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815308-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/envidat-lwf-87_2019-03-06",
-        "description": "Measurement of tree ring widts in tree stem disks according to 'Br\u00e4ker O.U. (1993) Anleitung zur Entnahme von Stammscheiben auf Ertragskundefl\u00e4chen'    ### Purpose: ###    tree growth",
-        "license": "proprietary"
-    },
-    {
-        "id": "envidat_232_1.0",
-        "title": "Reproducibility Dataset for CRYOWRF v1.0",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815061-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815061-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/envidat_232_1.0",
-        "description": "This repository contains data required for reproducibility of the results to be published in the associated manuscript.     Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.     The datasets consist of two folders organized in zip format:     1. REPRODUCIBILITY_SIMULATION:        Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript      Additional files include datasets (from IMAU-FDM / RACMO, see \"credits\" below ) as well as helper python scripts to       produce *.sno files       which are used as initial conditions for SNOWPACK in CRYOWRF.     2. REPRODUCIBILITY_POSTPROCESSING:        Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.     Each of the folders have their own readme files for more details.     ### Code  citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165    location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF     (stable releases / institutional repo) https://github.com/vsharma-next/CRYOWRF       (dev branches  / developer repo)      ### Publication  **Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**  Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development    ### Acknowledgements  We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:    * Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. No\u00ebl, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015     * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. No\u00ebl, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016).  The Cryosphere, 12, doi:10.5194/tc-12-1643-2018",
-        "license": "proprietary"
-    },
-    {
-        "id": "environmental-constraints-on-tree-growth_1.0",
-        "title": "Environmental constraints on tree growth",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815329-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815329-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/environmental-constraints-on-tree-growth_1.0",
-        "description": "Seasonal variation in environmental constraints (vapor pressure deficit \u2013 VPD, air temperature, and soil moisture) on tree growth for the potential distribution range of seven widespread Central European tree species. We simulated environmental constraints on growth fusing 3-PG model or the species\u2019 potential distribution range within the forested area of Switzerland on a 1\u00d71 km grid for seven dominant tree species: _Larix decidua_, _Picea abies_, _Abies alba_, _Fagus sylvatica_, _Acer pseudoplatanus_, _Pinus sylvestris_, and _Quercus robur_. For this purpose, we simulated the growth of these tree species in monocultures with the average climate observed during 1961\u20131990 or 1991-2018. The stands were initialized as 2-year-old plantations with an initial density of 2,500 trees ha-1 and simulated until the age of 30 years. For each simulated month, we obtained the relative contribution of environmental constraints (VPD, temperature, and soil water) on tree growth.",
-        "license": "proprietary"
-    },
     {
         "id": "environmental_layers_1",
         "title": "Marine environmental data layers for Southern Ocean species distribution modelling",
@@ -208870,32 +203930,6 @@
         "description": "This data set, ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990, contains both the original ERBE albedo data at 2.5 degree spatial resolution, and the International Land Surface Climatology Project Initative II (ISLSCP Initiative II) albedo product re-gridded to 1 degree resolution. The goals of the ERBE were (1) to understand the radiation balance between the Sun, Earth, atmosphere, and space and (2) to establish an accurate, long-term baseline data set for detection of climate changes. Earth Radiation Budget (ERB) data are fundamental to the development of realistic climate models and to the understanding of natural and anthropogenic perturbations of the climate system. As part of ERBE, measurements of broadband shortwave radiation reflected from the Earth-atmosphere system were obtained, from which top of atmosphere albedo values were calculated. In addition, values from scenes determined to be free of clouds were analyzed separately and clear-sky albedos were derived. For this study, only the clear-sky albedos are included. The ERBE data sets for ISLSCP Initiative II contain global, top of atmosphere, clear sky albedo data from January 1986 to February 1990.",
         "license": "proprietary"
     },
-    {
-        "id": "escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0",
-        "title": "Escarpment evolution drives the diversification of the Madagascar flora",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "42.3632812, -26.6684045, 51.7675781, -11.3522326",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082034-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082034-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0",
-        "description": "Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.",
-        "license": "proprietary"
-    },
-    {
-        "id": "espon-digiplan_1.0",
-        "title": "ESPON Digiplan",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "6.5478516, 46.0244304, 14.3701172, 54.6331536",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815052-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815052-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/espon-digiplan_1.0",
-        "description": "The dataset as a part of the international project ESPON Digiplan. The aim of this international project is to assess the extent, organisation and financing of digitisation of plan data as well as the use of these data in ESPON member countries. As a part of the in-depth case study, 7 virtual expert interviews in Switzerland and 5 virtual expert interviews in Germany were conducted with experts on the topic of digitisation of plan data. The documents contain the transcripts of the interviews. The transcripts aim to capture the content of the interviews, which is why voice raising and lowering, as well as pauses in the interview, were not specifically recorded. The interviews were conducted in German, therefore the transcripts are also in German.",
-        "license": "proprietary"
-    },
     {
         "id": "eta_model_723_1",
         "title": "SAFARI 2000 ETA Atmospheric Model Data, Wet and Dry Seasons 2000",
@@ -208909,45 +203943,6 @@
         "description": "With modern computer power now capable of making mesoscale model output available in real time in the operational environment, increased attention has been given to utilizing these models in order to improve the forecasting ability of meteorologists. The National Centers for Environmental Prediction (NCEP) has developed a step-mountain eta coordinate model generally known as the ETA Model.This NCEP ETA data assimilation and prediction system (see Mesinger et al., 1988; Black, 1994) has been used by the South African Weather Bureau/Service (SAWS) to provide operational regional forecast guidance since November 1993. SAWS used this model to produce the basic meteorological data for the SAFARI project. The SAWS ETA model is a hydrostatic model with a horizontal grid spacing of approximately 48 km and 38 vertical levels, with layer depths that range from 20 m in the planetary boundary layer to 2 km at 50 mb. There have been several major ETA Model upgrades at SAWS: in March 1996, August 1998, November 1999, and August 2001.",
         "license": "proprietary"
     },
-    {
-        "id": "eur11_1.0",
-        "title": "High resolution climate data for Europe",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "-44.6418061, 21.958194, 64.9248601, 72.6081938",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815092-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815092-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/eur11_1.0",
-        "description": "High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30\u2009arc\u2009sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.    The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.",
-        "license": "proprietary"
-    },
-    {
-        "id": "european-snow-booklet_1.0",
-        "title": "European Snow Booklet \u2013 an Inventory of Snow Measurements in Europe",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-25.9936523, 33.4693296, 67.0605469, 71.5321027",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815117-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815117-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/european-snow-booklet_1.0",
-        "description": "The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 \u201cA European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)\u201d. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.    For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).",
-        "license": "proprietary"
-    },
-    {
-        "id": "evoltree-conference-2021-birmensdorf-switzerland_1.0",
-        "title": "Genomics and Adaptation in Forest Ecosystems. Book of Abstracts. EvolTree Conference 2021, 14 \u2013 17 September 2021, Birmensdorf, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4549656, 47.3607695, 8.4549656, 47.3607695",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815129-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815129-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/evoltree-conference-2021-birmensdorf-switzerland_1.0",
-        "description": "The first EVOLTREE Conference, taking place in hybrid format (on-site and online) at WSL Birmensdorf (Switzerland) from 14-17 September, 2021, focuses on the genomics of trees and interacting species from evolutionary, demographic, and ecological perspectives.    EVOLTREE is a European network of institutions engaged in studying the evolution and functioning of forest ecosystems, in particular trees as the foundation species in forest stands. A prime topic in the face of ongoing climate change is to elucidate how trees, together with their associated organisms such as mycorrhizal fungi, respond to rapid environmental changes.    The conference includes contributions that apply innovative approaches and consider the relevance of their research in the context of biodiversity conservation through natural dynamics or silvicultural interference.",
-        "license": "proprietary"
-    },
     {
         "id": "ewing_0",
         "title": "Measurements made near South Africa in 2001",
@@ -208961,45 +203956,6 @@
         "description": "Measurements made near South Africa in 2001.",
         "license": "proprietary"
     },
-    {
-        "id": "example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0",
-        "title": "Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "11.3475037, 47.5904203, 11.5795898, 47.7245445",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815147-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815147-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0",
-        "description": "This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of M\u00fcnchen, at the edge of Bavarian Alps).  The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.  The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.     Additional notes for vector data:   Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API -  https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any  perimeter or area can be digitised freely using the QGIS editing toolbar.    The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:    - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service \u201cwith funding by the European Union\u201d based on SRTM and ASTER GDEM)  - Digitales Gel\u00e4ndemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung \u2013 www.geodaten.bayern.de \u2013and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif    This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.    Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.    This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.",
-        "license": "proprietary"
-    },
-    {
-        "id": "experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0",
-        "title": "Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "8.7007642, 46.6518076, 8.7037575, 46.6540464",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815165-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815165-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-rockfall-dataset-tschamut-grisons-switzerland_1.0",
-        "description": "# Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.     The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30\u201380 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of     * Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)    * Digital Elevation Model (grid resolution 2 m) obtained via UAV  * Orthophoto (5 cm resolution) obtained via UAV  * Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)  * StoneNode v1.0 raw data stream for equipped rocks.    Further information is found in    * __A. Caviezel__ et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/  * __ P. Niklaus__ et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/",
-        "license": "proprietary"
-    },
-    {
-        "id": "experimental-rockfall-trilogy-of-surava_1.0",
-        "title": "Experimental rockfall trilogy of Surava",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.5958281, 46.6544588, 9.61411, 46.6624116",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815192-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815192-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/experimental-rockfall-trilogy-of-surava_1.0",
-        "description": "We performed an experimental trilogy of induced rockfall experiments in a spruce stand in Surava (CH) within (i) the original forest, (ii) after a logging job, resulting in lying deadwood and (iii) the cleared, deadwwod-free state. The three experimental set-ups allow quantifying the deadwood effect on overall rockfall risk for the same forest (slope, species) in three different conditions.",
-        "license": "proprietary"
-    },
     {
         "id": "explorer_0",
         "title": "Measurements made near the Cayman Islands between 2001 and 2003",
@@ -209260,45 +204216,6 @@
         "description": "Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.",
         "license": "proprietary"
     },
-    {
-        "id": "face-stillberg_1.0",
-        "title": "FACE: Stillberg CO2 enrichment and soil warming study",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.867544, 46.7716544, 9.867544, 46.7716544",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815224-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815224-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/face-stillberg_1.0",
-        "description": "# Background information    High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 \u00b0C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project.    # Experimental design    The experiment consisted of 40 hexagonal 1.1 m\u00b2 plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species.  At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.    # Data description    Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements.",
-        "license": "proprietary"
-    },
-    {
-        "id": "factors-influencing-teenagers-forest-visit-frequency_1.0",
-        "title": "Factors influencing teenagers' forest visit frequency",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815249-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815249-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/factors-influencing-teenagers-forest-visit-frequency_1.0",
-        "description": "The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.",
-        "license": "proprietary"
-    },
-    {
-        "id": "factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0",
-        "title": "Factors slowing down upward shifts of trees\u2019 upper elevation limits",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815267-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815267-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0",
-        "description": "Species range limits are expected to be dramatically altered under future climate change and many species are predicted to shift their distribution upslope to track their suitable conditions (i.e. based on their niche). However, there might be large discrepancies between the speed of the upward shift of the climatic niche and the actual migration velocity of the species, especially in long-lived organisms such as trees. Here, we compared the simulations of the upslope displacement of the bioclimatic envelope of 16 tree species inhabiting temperate mountain forests under ongoing and future climate change obtained by correlative species distribution models (SDMs) to those from a dynamic forest model accounting for dispersal, competition and demography. We then partitioned the discrepancy in upslope migration velocity between the SDMs and the dynamic forest model into different components by manipulating dispersal limitation, interspecific competition and demography.     This dataset contains the calibration and evaluation data used to create the bioclimatic envelope models, the predictors for the future scenarios (raster layers) and the bioclimatic input data used in the dynamic forest models used in the following publication (Scherrer et al. 2020).     Paper Citation:  Scherrer, D., Vitasse, Y., Guisan , A., Wohlgemuth, T., & Lischke, H. (2020). Competition and demography rather than dispersal limitation slow down upward shifts of trees\u2019 upper elevation limits in the Alps. Journal of Ecology, in press.",
-        "license": "proprietary"
-    },
     {
         "id": "fasir_biophys_monthly_xdeg_970_1",
         "title": "ISLSCP II FASIR-adjusted NDVI Biophysical Parameter Fields, 1982-1998",
@@ -209351,32 +204268,6 @@
         "description": "A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999.  The areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time).  While cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/).  Initial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel.  A number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996).  In addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins.  We examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002).  The effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).",
         "license": "proprietary"
     },
-    {
-        "id": "fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0",
-        "title": "Fatal avalanche accidents in Switzerland since 1936/37",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082513-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082513-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0",
-        "description": "**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.    This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one fatality. The data set commences on 01/10/1936. After the completion of a hydrological year, the new data is added.    The following information is provided:     * avalanche identifier      * date of the accident     * accuracy of the date in range of days before and after    * hydrological year (always from first of october to end of september)   * canton     * municipality     * start zone point latitude   * start zone point longitude   * start zone point accuracy (in meters)   * start zone point elevation (in meteres above sea level)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)       * forecasted avalanche danger level 1 (first danger)     * forecasted avalanche danger level 2 (second danger)   * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)    * number of dead persons      * number of caught persons     * number of fully buried persons     * activity/location of the accident party at the time of the incident",
-        "license": "proprietary"
-    },
-    {
-        "id": "fatal-avalanche-accidents-switzerland-1995_1.0",
-        "title": "Fatal avalanche accidents in Switzerland since 1995-1996",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815287-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815287-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/fatal-avalanche-accidents-switzerland-1995_1.0",
-        "description": "Attention: this data is not updated after 2022 anymore. This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.    The following information is provided:       * avalanche identifier     * date of the accident    * accuracy of the date in range of days before and after    * canton    * name of the locality    * start zone of the avalanche      * coordinates (Swiss coordinate system, approximately in middle of start zone)      * accuracy of the coordinates in meters   * elevation (in meteres above sea level, app. in middle of start zone)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)      * number of dead persons     * number of caught persons    * number of fully buried persons      * forecasted avalanche danger level     * activity/location of the accident party at the time of the incident",
-        "license": "proprietary"
-    },
     {
         "id": "fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA",
         "title": "MERIS - Water Parameters - North Sea, Monthly",
@@ -209429,19 +204320,6 @@
         "description": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.  Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.",
         "license": "proprietary"
     },
-    {
-        "id": "fdp-grapevine-trunks-impact-xylem-phloem_1.0",
-        "title": "Impact of the \u201cFlavescence dor\u00e9e\u201d phytoplasma on xylem growth and phloem anomalies in trunks of \u2018Chardonnay\u2019 grapevines (Vitis vinifera)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.9400816, 46.042408, 8.9438152, 46.04494",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815299-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815299-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-grapevine-trunks-impact-xylem-phloem_1.0",
-        "description": "Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the \"Flavescence dor\u00e9e\" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R.  The Flavescence dor\u00e9e phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar \u2018Chardonnay\u2019 in order to verify their response to FDp infections.   In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens.",
-        "license": "proprietary"
-    },
     {
         "id": "fe651dbef5d44248bef70906f4b3d12b_NA",
         "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1",
@@ -209728,32 +204606,6 @@
         "description": "Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.",
         "license": "proprietary"
     },
-    {
-        "id": "fiber-bundle-model-for-snow-failure_1.0",
-        "title": "Fiber Bundle Model for snow failure and concurrent Acoustic Emissions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8473066, 46.8125512, 9.8473066, 46.8125512",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815051-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815051-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/fiber-bundle-model-for-snow-failure_1.0",
-        "description": "This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.    The data underlay the figures in the publication \"Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model\" submitted for publication to \"Geophysical Research Letters\".",
-        "license": "proprietary"
-    },
-    {
-        "id": "field-observations-of-snow-instabilities_1.0",
-        "title": "Field observations of snow instabilities",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.7084808, 46.6864249, 10.0174713, 46.8979737",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815084-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815084-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/field-observations-of-snow-instabilities_1.0",
-        "description": "This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.    These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.     The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.    These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.    ### Acknowlegements  Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas D\u00fcrr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.",
-        "license": "proprietary"
-    },
     {
         "id": "fieldsunp_65_1",
         "title": "Optical Thickness Data: Ground (OTTER)",
@@ -211067,19 +205919,6 @@
         "description": "The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) ",
         "license": "proprietary"
     },
-    {
-        "id": "fire-randomizer-first-release_1.0",
-        "title": "fire-randomizer: first release",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "8.4545978, 47.3606372, 8.4545978, 47.3606372",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082141-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082141-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/fire-randomizer-first-release_1.0",
-        "description": "Tool\u00a0to assess fire selectivity for topographic (e.g. alitiude, slope, aspect) or land use (forest or vegetation type, distance to infrastructures) categories with Monte Carlo simulations.",
-        "license": "proprietary"
-    },
     {
         "id": "fire_emissions_724_1",
         "title": "SAFARI 2000 Fire Emission Data, Dry Season 2000",
@@ -211132,19 +205971,6 @@
         "description": "The broadscale distribution of flora (lichens, mosses, non-marine algae)and fauna (penguins, flying birds, seals)in the Stillwell Hills was mapped using GPS technology. Samples of flora were collected for taxonomic identification.  Data were recorded and catalogued in shapefiles.",
         "license": "proprietary"
     },
-    {
-        "id": "flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0",
-        "title": "Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "1.6699219, 46.5588603, 27.5097656, 59.7120972",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815113-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815113-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0",
-        "description": "Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.",
-        "license": "proprietary"
-    },
     {
         "id": "fltrepepoch_1",
         "title": "Flight Reports EPOCH",
@@ -211158,19 +205984,6 @@
         "description": "The Flight Reports EPOCH dataset consists of flight number, purpose of flight, and flight hours logged during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The mission reports are available from July 27, 2017 through August 31, 2017 in PDF format. ",
         "license": "proprietary"
     },
-    {
-        "id": "flu-a-bh_1.0",
-        "title": "Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.9451, 46.7479, 9.9451, 46.7479",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815125-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815125-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/flu-a-bh_1.0",
-        "description": "Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26\u00b0) North-east slope (45\u00b0). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.",
-        "license": "proprietary"
-    },
     {
         "id": "fluxnet_point_1029_1",
         "title": "ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites",
@@ -211197,149 +206010,6 @@
         "description": "Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92.  Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season.  Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick.  This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.  The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season.",
         "license": "proprietary"
     },
-    {
-        "id": "forclim_4.0",
-        "title": "ForClim",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815136-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815136-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forclim_4.0",
-        "description": "ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the \"GNU GENERAL PUBLIC LICENSE v3\" license.    ![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg \"ForClim structure\")",
-        "license": "proprietary"
-    },
-    {
-        "id": "forecast-avalanche-danger-level-european-alps-2011-2015_1.0",
-        "title": "Forecast avalanche danger level European Alps 2011 - 2015",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "4.8779297, 43.2761391, 16.2597656, 48.179762",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815158-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815158-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forecast-avalanche-danger-level-european-alps-2011-2015_1.0",
-        "description": "This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).  For details on the data please refer to this publication.  The dataset contains the following:   - shape files for the warning regions in the Alps  - highest forecast danger level for each warning region and day",
-        "license": "proprietary"
-    },
-    {
-        "id": "forecomon-proceedings_v14",
-        "title": "Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7\u20139 June 2021, Birmensdorf, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4549183, 47.3607533, 8.4549183, 47.3607533",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815176-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815176-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forecomon-proceedings_v14",
-        "description": "Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings.  FORECOMON 2021 - the 9th forest ecosystem monitoring conference.  7-9 June 2021, WSL, Birmensdorf, Switzerland    The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed.    Copyright \u00a9 2021 by WSL, Birmensdorf  The authors are responsible for the content of their contribution.",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest-radiation-data_1.0",
-        "title": "Shading by Trees and Fractional Snow Cover Control the Subcanopy Radiation Budget",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8737264, 46.8433152, 9.8778033, 46.8451938",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815272-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815272-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/forest-radiation-data_1.0",
-        "description": "This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper \"Shading by trees and fractional snow cover control the sub-canopy radiation budget\", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland.",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest-reserves-monitoring-in-switzerland_1.0",
-        "title": "Forest Reserves Monitoring in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "6.23634, 46.13293, 10.35923, 47.77037",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815284-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815284-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forest-reserves-monitoring-in-switzerland_1.0",
-        "description": "Long term monitoring of natural forests provides insights into ecological processes shaping forests without human intervention. To study natural forest dynamics, the former chair of silviculture at the Swiss Federal Institute of Technology (ETH) initiated a network of forest reserves in the late 1940's.    Since 2006, the monitoring is carried out in a cooperation project of  the chair of Forest Ecology at ETH, the Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL) and the Federal Office for the Environment (FOEN). The project relaunch led to a streamlining of the reserve network, which now contains 33 of the original reserves and 16 new reserves.     The main goal is to evaluate the effectiveness of the federal reserve policy by analysing to what extent forest reserves differ from managed forests in terms of structure, dynamics, and habitat quality.",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest-snow-model-fluela_1.0",
-        "title": "Input datasets for forest snow modelling in Fluela valley, WY 2016-21",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8025513, 46.764199, 9.9838257, 46.8394031",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082624-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082624-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-snow-model-fluela_1.0",
-        "description": "This dataset contains surface datasets (in particular canopy structure fields) and meteorological input (water years 2016-2021) required to run the snow model FSM2 over the Fluela valley. Land surface datasets are available for a 1.5x2.5km model domain at 2m spatial resolution, meteorological input at hourly resolution is provided for a point and corresponds to the location of the automatic weather station / snow measurement field 5DF in Davos. Corresponding FSM2 simulations are used and analyzed in the publication 'Canopy structure, topography and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests' by Mazzotti et al. (submitted to HESSD). This publication should be cited whenever the dataset is used.",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest-snow-modelling-davos-2012-2015_1.0",
-        "title": "Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815307-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815307-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/forest-snow-modelling-davos-2012-2015_1.0",
-        "description": "This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.  This publication must be cited when using this dataset.",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest-type-nfi_2018 (current)",
-        "title": "Forest Type NFI",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815316-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815316-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forest-type-nfi_2018%20(current)",
-        "description": "Two versions of the data are currently available: 2018 and 2016.    The 2018 version presents a remote sensing-based approach for a countrywide mapping of the dominant leave type (DLT) with the two classes broadleaved and coniferous in Switzerland. The spatial resolution is 10 m with the fraction of the class broadleaf. The classification approach incorporates a random forest classifier, explanatory variables from multispectral Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the tree type map with independent NFI data revealed deviations in mixed stands.    In the 2016 version (3 m spatial resolution), the classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of 3.17%).",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest_area-44_1.0",
-        "title": "Forest area",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815205-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815205-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forest_area-44_1.0",
-        "description": "The forest area is the total sum of all areas classified as forest according to NFI\u2019s forest definition. The forest definition includes shrub forest.  This theme is also used to assess the total area when forest and non-forest need to be distinguished.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest_area_by_forest_function-262_1.0",
-        "title": "Forest area by forest function",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815235-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815235-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forest_area_by_forest_function-262_1.0",
-        "description": "The forest area refers to all areas classified as forest according to NFI\u2019s forest definition. The forest definition includes shrub forest.   For each forest function (including no special forest function) identified in the survey of the forestry services, the size of the associated forest area is displayed. One forest region may fulfil several different forest functions and may thus contribute to the forest area for several forest functions.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "forest_area_by_natural_hazard-260_1.0",
-        "title": "Forest area by natural hazard",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815257-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815257-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forest_area_by_natural_hazard-260_1.0",
-        "description": "For each natural hazard process according to FOEN\u2019s SilvaProtectCH, the size of the forest area affected is given. One forest region may be affected by several different natural hazard processes and may thus contribute to the forest area affected by several different natural hazard processes.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "forest_carbon_flux_949_1",
         "title": "Global Forest Ecosystem Structure and Function Data For Carbon Balance Research",
@@ -211353,32 +206023,6 @@
         "description": "A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.",
         "license": "proprietary"
     },
-    {
-        "id": "forhycs-v-1-0-0-model-code_1.0.0",
-        "title": "FORHYCS v. 1.0.0 model code",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815053-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815053-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/forhycs-v-1-0-0-model-code_1.0.0",
-        "description": "Model code, technical documentation and auxiliary files for the dynamic ecohydrological model FORHYCS (FORests and HYdrology under Climate change in Switzerland). FORHYCS combines two pre-existing models, the hydrological model PREVAH and the forest landscape model TreeMig.     License: GPL v3",
-        "license": "proprietary"
-    },
-    {
-        "id": "four-years-of-daily-stable-water-isotope-data_1.0",
-        "title": "Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.6654663, 47.0120984, 8.7574768, 47.1523693",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815097-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815097-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/four-years-of-daily-stable-water-isotope-data_1.0",
-        "description": "This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.",
-        "license": "proprietary"
-    },
     {
         "id": "fram25k_1",
         "title": "Framnes Mountains 1:25000 Topographic GIS Dataset",
@@ -211496,19 +206140,6 @@
         "description": "This data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs. The Production Efficiency Model (PEM) was used to produce the estimated fuel loads for southern Africa for the 1999-2000 growing seasons.",
         "license": "proprietary"
     },
-    {
-        "id": "full-content-of-wsl-fauna-database_1.0",
-        "title": "Full content of WSL Fauna Database",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082225-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082225-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/full-content-of-wsl-fauna-database_1.0",
-        "description": "Complete extract of Fauna Database of WSL, containing all projects and all taxa. Meant as exchange and citation platform for sharing the data with the national data centre 'Centre Suisse de la Cartographie de la Fauna (CSCF)', and Info Fauna.",
-        "license": "proprietary"
-    },
     {
         "id": "g3acld_003",
         "title": "SAGE III Meteor-3M L2 Monthly Cloud Presence Data (HDF-EOS) V003",
@@ -211899,45 +206530,6 @@
         "description": "The Australian Antarctic Gazetteer is maintained by the Australian Antarctic Data Centre  and the Secretary of the Australian Antarctic Division Place Names Committee. It contains information about names in the Australian Antarctic Territory and the Territory of Heard Island and McDonald Islands. Users can search by place name, region, feature type, latitude or longitude. Displayed information includes a descriptive narrative, and where available, an image, source information and altitude.  Users can download the whole gazetteer or their search results as a KML or CSV file.",
         "license": "proprietary"
     },
-    {
-        "id": "gbif-range-r_0.2",
-        "title": "gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082333-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082333-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/gbif-range-r_0.2",
-        "description": "Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)",
-        "license": "proprietary"
-    },
-    {
-        "id": "gcnet_1.0",
-        "title": "Greenland Climate Network (GC-Net) Data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "-69.2578125, 58.9288406, -10.1953125, 83.2212265",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815145-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815145-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/gcnet_1.0",
-        "description": "## In Memory of Dr. Konrad (Koni) Steffen    <br /> <br />  Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).  Starting October 3, 2022, the access to the latest versions of the \"ready to use\" L1 data has been migrated to GEUS. Future data versions will be available at:  [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)  ### Background   Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was \"Swiss Camp\" or the \"ETH-CU\" camp (GC-Net station #01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see \"C-file\" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.    ### Overview   Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.    ### The standard GC-Net station consists of:   * Air temperature measurements at 2 heights above the surface * Temperature and humidity measurements at 2 heights above the surface * Wind speed and direction measured at 2 heights above the surface * Sonic distance sounder measurements for 2 snow height and distance of instruments to surface * Incoming shortwave radiation measurement * Reflected shortwave radiation measurement * Net broadband radiation (long- and short-wave) measurement * Air pressure measurement  Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.    ### Dataset   This dataset consists of 2 main data levels; Level 0 and Level 1.  Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni\u2019s personal data archive. Level 0 data (.zip) directories contain subdirectories:   * \u201cC file\u201d - contains the historical processed datafile for each station.  * \u201cCampbell logger files\u201d - contains the raw csv datafiles from the stations\u2019 Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).  * \u201cPhotos\u201d - contains photographs of the station when available marked by year.  Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):  [GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing \"GC-Net-level-1-data-processing\")  Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead \"NEAD format\").  <br />  ### Additional Details   Dataset description publication will be forthcoming.  The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.",
-        "license": "proprietary"
-    },
-    {
-        "id": "gcos-swe-data_1",
-        "title": "GCOS SWE data from 11 stations in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "7.7511649, 46.0234031, 10.4193435, 46.8605795",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815162-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815162-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/gcos-swe-data_1",
-        "description": "This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station. The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes: \u2022 Raw data are archived in the snow and avalanche database at SLF. \u2022 Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation. \u2022 Historical data of these 11 stations have been digitized and all data have been quality controlled.  \u2022 Detailed metadata (location of measurements) are available. \u2022 Data gaps for the two most important winter and spring dates were reconstructed based on a published SWE parameterization from co-located snow depth measurements. \u2022 Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).",
-        "license": "proprietary"
-    },
     {
         "id": "gdp_xdeg_974_1",
         "title": "ISLSCP II Global Gridded Gross Domestic Product (GDP), 1990",
@@ -211951,45 +206543,6 @@
         "description": "The data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990.Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects.  This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN. ",
         "license": "proprietary"
     },
-    {
-        "id": "gem-bh_1.0",
-        "title": "Processed permafrost borehole data (2940 m asl), Gemsstock, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "8.61026, 46.60097, 8.61026, 46.60097",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815206-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815206-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/gem-bh_1.0",
-        "description": "Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50&deg;) North-West slope (315&deg;). The surface material is bedrock and borehole depth is 40 m.  Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.    __Publications__    1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579\u2013597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101.    2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157\u2013164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.",
-        "license": "proprietary"
-    },
-    {
-        "id": "gem2_1.0",
-        "title": "GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "8.60904, 46.60369, 8.60904, 46.60369",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815190-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815190-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/gem2_1.0",
-        "description": "Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.    Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites",
-        "license": "proprietary"
-    },
-    {
-        "id": "generalised-stand-descriptions-within-the-swiss-nfi_1.0",
-        "title": "Generalised stand descriptions in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815225-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815225-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/generalised-stand-descriptions-within-the-swiss-nfi_1.0",
-        "description": "The files refer to the data and R code used in Mey et al. \"From small forest samples to generalised uni- and bimodal stand descriptions\" (2021) _Methods in Ecology and Evolution_.    __Generalised stand descriptions__ are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data.    The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).    ---------------------------------------    The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper.    The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.    The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.",
-        "license": "proprietary"
-    },
     {
         "id": "geocoord_556_1",
         "title": "BOREAS Site and Area Geographic Coordinate Information",
@@ -216228,19 +210781,6 @@
         "description": "The Australian Antarctic Data Centre's GIS data of Mawson Station was updated in 2004 using a map image provided by Dr Malcolm Arnold who wintered at the station during that year.  The data are included in the data available for download from a Related URL below.  The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.  Data described by this metadata record has Dataset_id = 45.  Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.",
         "license": "proprietary"
     },
-    {
-        "id": "gisdata_1.0",
-        "title": "Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815253-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815253-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_1.0",
-        "description": "The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the \"Cross Blended Hypso with Shaded Relief and Water\". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.    Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com",
-        "license": "proprietary"
-    },
     {
         "id": "giss_wetlands_632_1",
         "title": "SAFARI 2000 Wetlands Data Set, 1-Deg (Matthews and Fung)",
@@ -216254,19 +210794,6 @@
         "description": "This database provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. The subset retains all five arrays at the 1-degree resolution but only for the area of interest. The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type.",
         "license": "proprietary"
     },
-    {
-        "id": "gl_microclim_1.0",
-        "title": "Greenland shrubs and microclimate",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-52.35655, 64.03434, -49.71769, 65.01146",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815278-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815278-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/gl_microclim_1.0",
-        "description": "## Study Aim    We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.    ## Data Description    Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.     ### Nuup Kangerlua (Godth\u00e5bsfjord)    _Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness    ### Kangerluarsunnguaq (Kobbefjord)    Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height       ## Methods    ### Field survey in Nuup Kangerlua    We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).     ### Field survey in Kangerluarsunnguaq     We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).    ### Biotic variables    We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.    ### Climate variables     We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June \u2013 August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March \u2013 May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).    ### Topography and terrain wetness indicator variables    We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how \u2018wet\u2019 an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.     ### Computer code    Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)    ### Third-party data    Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).",
-        "license": "proprietary"
-    },
     {
         "id": "glacio_1973_barometric_levelling_1",
         "title": "Barometric Levelling over IAGP trilateration net, Wilkes Land 1973",
@@ -216475,19 +211002,6 @@
         "description": "A report of the data collected from the 1986 Glaciology program at Casey. Includes measurements of ice movement, accumulation, snow temperature, gravity, magnetic, weather data, surface density and hardness, and a summary of all known measurements along the A, B and Undulation Lines on Law Dome.  These documents have been archived in the records store at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "glide-snow-avalanche-activity-on-dorfberg-davos_1.0",
-        "title": "Glide-snow avalanche activity on Dorfberg, Davos, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.8270988, 46.8077793, 9.8497581, 46.8265749",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082548-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082548-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/glide-snow-avalanche-activity-on-dorfberg-davos_1.0",
-        "description": "This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:    Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91    We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.",
-        "license": "proprietary"
-    },
     {
         "id": "glider_0",
         "title": "Glider measurements near Tampa, FL",
@@ -216527,32 +211041,6 @@
         "description": "The GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products consist of full disk extent gridded lightning flash data collected by the Geostationary Lightning Mapper (GLM) onboard the Geostationary Operational Environmental Satellite 16 and 17 (GOES-16 and GOES-17). These satellites are a part of the GOES-R series program: a four satellite series within the National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Association (NOAA) GOES program. GLM is the first operational geostationary optical lightning detector that provides total lightning data (in-cloud, cloud-to-cloud, and cloud-to-ground flashes). While it detects each of these types of lightning, the GLM is unable to distinguish between each type. The GLM GOES L3 dataset files contain gridded lightning flash data over the Western Hemisphere in netCDF-4 format from December 31, 2017 to present as this is an ongoing dataset.",
         "license": "proprietary"
     },
-    {
-        "id": "global-cryosphere-watch-data-survey_1.0",
-        "title": "Global Cryosphere Watch data survey",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815290-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815290-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/global-cryosphere-watch-data-survey_1.0",
-        "description": "Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).",
-        "license": "proprietary"
-    },
-    {
-        "id": "global-species-distributions-for-mammals-reptiles-and-amphibians_1.0",
-        "title": "Global species distributions for mammals, reptiles, and amphibians",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082087-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082087-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/global-species-distributions-for-mammals-reptiles-and-amphibians_1.0",
-        "description": "We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5\u00b0 spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.",
-        "license": "proprietary"
-    },
     {
         "id": "global_N_cycle_797_1",
         "title": "Global N Cycle: Fluxes and N2O Mixing Ratios Originating from Human Activity",
@@ -217112,19 +211600,6 @@
         "description": " We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors.  Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment.  ",
         "license": "proprietary"
     },
-    {
-        "id": "gone-wild-grapevines-in-forests_1.0",
-        "title": "Gone-wild grapevines in forests may act as a potential habitat for \u201cFlavescence dor\u00e9e\u201d phytoplasma vectors and inoculum",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.4347534, 45.8809865, 9.2422485, 46.5159373",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082143-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082143-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/gone-wild-grapevines-in-forests_1.0",
-        "description": "Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dor\u00e9e phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, <i>Scaphoideus titanus</i> and <i>Orientus ishidae</i>. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors.  The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both <i>S.titanus</i> and <i>O.ishidae</i>. Eventually, the need to adapt the current FD management strategies are highlighted.",
-        "license": "proprietary"
-    },
     {
         "id": "gov.noaa.ncdc:C00842_Version 1.2",
         "title": "Blended 6-Hourly Sea Surface Wind Vectors and Wind Stress on a Global 0.25 Degree Grid (1987-2011)",
@@ -231529,32 +226004,6 @@
         "description": "The Global Primary Production Data Initiative (GPPDI) was set up as a Focus 1 activity of the IGBP Data and Information System, a coordinated international program to improve worldwide estimates of terrestrial net primary productivity (NPP) for parameterization, calibration, and validation of NPP models at various scales.The GPPDI data collection contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The point measurements of NPP were categorized as either Class A, representing intensively studied or well-documented study sites (e.g., with site-specific climate, soils information, etc.), Class B, representing more numerous extensive sites with less documentation and site-specific information available, or Class C, representing regional collections of half-degree latitude-longitude grid cells. This data set in the ISLSCP II collection represents the GPPDI Class B NPP data. The Class B NPP data file contains biomass dynamics, climate, and site-characteristics data georeferenced to each site. There is one ASCII data file with this data set. ",
         "license": "proprietary"
     },
-    {
-        "id": "gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0",
-        "title": "GPS-derived data of SWE, HS and LWC and corresponding validation data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.8093963, 46.8295131, 9.8093963, 46.8295131",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815057-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815057-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0",
-        "description": "This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.    The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.    Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:   > Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431",
-        "license": "proprietary"
-    },
-    {
-        "id": "grassland-use-intensity-maps-for-switzerland_1.0",
-        "title": "Grassland-use intensity maps for Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082226-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082226-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/grassland-use-intensity-maps-for-switzerland_1.0",
-        "description": "A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018\u20132021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.    The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m \u00d7 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.    We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).    The webcam-based reference data used for verification was subsequently added on 14.02.2024.",
-        "license": "proprietary"
-    },
     {
         "id": "gravity_wilkes_1964_1",
         "title": "Gravity Survey Results, Wilkes Ice Cap, 1964-65",
@@ -231568,19 +226017,6 @@
         "description": "The results of a gravity survey done on Wilkes Ice Cap. No information in the papers on how it was done, dates, etc - just the numbers. Even year is unsure (could be 1964 or 1965 season).  These documents have been archived at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0",
-        "title": "Green infrastructure in strategic spatial plans: Evidence from European urban regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-17.4023437, 33.5917433, 34.6289063, 68.4698482",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815116-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815116-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0",
-        "description": "The present dataset is part of the published scientific paper Gr\u0103dinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.  The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.  The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1\u201311) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12\u201336), was coded according to Table 1 of the article.  As a result, we provide the folowing outputs:  \u2022\tGI_Dataset_1_Items_1-12.xlsx \u2013 available on request  o\tResults of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option \u2013 this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.   \u2022\tGI_Dataset_2_Items_12-36.csv \u2013 freely available   o\tResults of the coding  on principles of GI planning followed in strategic plans of urban regions\u2013 this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.",
-        "license": "proprietary"
-    },
     {
         "id": "grinstedSBB-ECM-VIDEO_Not provided",
         "title": "2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen",
@@ -231906,19 +226342,6 @@
         "description": "The GRIP Hurricane and Tropical Storm Forecasts dataset consists of tropical cyclone model forecast tracks archived during the NASA Genesis and Rapid Intensification Processes (GRIP) field campaign. GRIP was one of three hurricane field campaigns conducted during the 2010 Atlantic/Pacific hurricane season. This tri-agency effort included NASA GRIP, the NSF Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) and the NOAA Intensity Forecasting Experiment 2010 (IFEX10). The hurricane and tropical storm forecasts data files are available from August 12 through November 14, 2010 in ASCII text format with browse files in KML format, viewable in Google Earth.  The ASCII text files contain 5-day model \u201cconsensus\u201d forecasts and the KML browse files contain model forecasts ranging from 5-days to 10-days.",
         "license": "proprietary"
     },
-    {
-        "id": "groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0",
-        "title": "Groundwater time series Studibach (Rinderer et al., 2019, WRR)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.7220859, 47.0382382, 8.7220859, 47.0382382",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815127-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815127-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0",
-        "description": "Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns \u2013 Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R",
-        "license": "proprietary"
-    },
     {
         "id": "gtopo30_hydro_1k_Not provided",
         "title": "GTOPO30 Hydro 1K",
@@ -231932,19 +226355,6 @@
         "description": "HYDRO1k is a geographic database developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale.",
         "license": "proprietary"
     },
-    {
-        "id": "gtree_1.0",
-        "title": "G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.86624, 46.771906, 9.86624, 46.771906",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815146-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815146-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZmllbGQgb2JzZXJ2YXRpb25zIG9mIHNub3cgaW5zdGFiaWxpdGllc1wiLFwiRU5WSURBVFwiLFwiZmllbGQtb2JzZXJ2YXRpb25zLW9mLXNub3ctaW5zdGFiaWxpdGllc1wiLFwiMS4wXCIsMjc4OTgxNTA4NCw3XSIsInVtbSI6IltcImZpZWxkIG9ic2VydmF0aW9ucyBvZiBzbm93IGluc3RhYmlsaXRpZXNcIixcIkVOVklEQVRcIixcImZpZWxkLW9ic2VydmF0aW9ucy1vZi1zbm93LWluc3RhYmlsaXRpZXNcIixcIjEuMFwiLDI3ODk4MTUwODQsN10ifQ%3D%3D/gtree_1.0",
-        "description": "# Background information  Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.     # Experimental design  The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224\u2009cm\u2009\u00d7\u200945\u2009cm) were established at each site, which were randomly assigned to the 2\u2009\u00d7\u20092 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5\u2009cm\u2009\u00d7\u200928\u2009cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).     # Data description  All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013\u20132021).",
-        "license": "proprietary"
-    },
     {
         "id": "gts_precip_daily_xdeg_1001_1",
         "title": "ISLSCP II Gauge-Based Analyses of Daily Precipitation over Global Land Areas",
@@ -232387,19 +226797,6 @@
         "description": "Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.",
         "license": "proprietary"
     },
-    {
-        "id": "habitat-map-of-switzerland_1.0",
-        "title": "The Habitat Map of Switzerland v1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815160-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815160-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/habitat-map-of-switzerland_1.0",
-        "description": "Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse The FOEN funded project \u2018Developing a Habitat Map of Switzerland\u2019 conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification\u2019s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only.  Habitats are mapped through a variety of approaches that can be grouped as either: 1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo\u2019s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.",
-        "license": "proprietary"
-    },
     {
         "id": "hamsrcpex_1",
         "title": "High Altitude MMIC Sounding Radiometer (HAMSR) CPEX V1",
@@ -232712,19 +227109,6 @@
         "description": "Heard Island and McDonald Islands, vegetation layer. This is a polygon dataset stored in the Geographical Information System (GIS). The data represents approximately the areas of vegetation cover on these islands.",
         "license": "proprietary"
     },
-    {
-        "id": "heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0",
-        "title": "Plant-mediated effects of heavy metals and acid rain on feeding aphids and caterpillars",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.4562111, 47.3623623, 8.4562111, 47.3623623",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815177-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815177-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0",
-        "description": "In controlled model forest ecosystems young trees were exposed to heavy metals in the soil and to acid precipitation. On spruce trees Lymantria monacha caterpillars and Cinara pilicornis aphids and on willow Pterocomma pilosum aphids were reared and monitored. Developmental time and fecundity of L. monacha were recorded and in aphids colony growth was measured.",
-        "license": "proprietary"
-    },
     {
         "id": "heliemps_1",
         "title": "Effects of helicopter operations on emperor penguin chicks",
@@ -232764,32 +227148,6 @@
         "description": "This study aimed to quantify the effects of helicopter operations on Antarctic wildlife, with an emphasis on determining minimum safe over-flight altitudes and landing distances for a range of species.  An experimental approach was adopted whereby wildlife were exposed to helicopters either over-flying or landing at specific altitudes or distances while the behaviour, and in some cases physiology, of individual animals were recorded. Two types of helicopters were used in the study: a Sikorsky S-76 (twin engine) and a Squirrel AS350 (single engine). This metadata record relates to the responses of Adelie Penguins (Pygoscelis adeliae) over a number of phases of their breeding cycle.  The fields in this dataset are:  Time Action Date",
         "license": "proprietary"
     },
-    {
-        "id": "herb-layer-biomass-in-swiss-forests_1.0",
-        "title": "Herb layer biomass in Swiss forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "7.195934, 46.0807887, 9.1971004, 47.5101827",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815199-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815199-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/herb-layer-biomass-in-swiss-forests_1.0",
-        "description": "The purpose of this project was to develop a model to estimate herb layer biomass and carbon stock based on the categorical cover estimate on each NFI sample plot. To this end, biomass and cover of the six main plant groups in the herb layer were collected from 405 1x1 m subplots on 135 study sites (15 sites in 9 strata) which were selected based on a stratified sampling approach. To ensure consistency with NFI methodology, study sites corresponded to the design of regular NFI sample plots and plant cover was estimated by trained field-crew members. Based on the dry weight of the plant biomass and the cover estimate on each subplot, a linear regression model was developed and applied to estimate herb layer biomass on each NFI sample plot.",
-        "license": "proprietary"
-    },
-    {
-        "id": "high-resolution-static-data-for-wrf-over-switzerland_1.0",
-        "title": "High resolution static data for WRF over Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "4, 45, 12, 49",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815211-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815211-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/high-resolution-static-data-for-wrf-over-switzerland_1.0",
-        "description": "Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.",
-        "license": "proprietary"
-    },
     {
         "id": "highjump_scans_1",
         "title": "Digital images of Operation Highjump aerial photography",
@@ -232816,19 +227174,6 @@
         "description": "This file comprises five high-resolution records of 10Be concentration in snow from Law Dome, East Antarctica: DSS0102-pit, DSS0506-pit, DSS0506-core, DSS0809-core and DSS0910-core.  A single composite series is constructed from three of these records (DSS0506-core, DSS0809-core and DSS0102-pit), providing a monthly-resolved time-series of 10Be concentrations at DSS over the decade spanning 1999 to 2009.  This work was done as part of AAS 2384, AAS 3064 and AAS 1172.  A data update was provided by Jason Anderson on 2012-12-17.",
         "license": "proprietary"
     },
-    {
-        "id": "hillshade-for-vegetation-height-model-nfi_2016 (current)",
-        "title": "Hillshade for Vegetation Height Model NFI",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815239-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815239-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hillshade-for-vegetation-height-model-nfi_2016%20(current)",
-        "description": "Hillshade of the digital surface model (DSM), calculated from digital aerial stereo images. The image data was acquired by the Federal Office of Topography swisstopo. The resolution of the DSM is 1 m x 1 m.",
-        "license": "proprietary"
-    },
     {
         "id": "historic_cropland_xdeg_966_1",
         "title": "ISLSCP II Historical Croplands Cover, 1700-1992",
@@ -232868,19 +227213,6 @@
         "description": "This data set is a subset of a global croplands data set (Ramankutty and Foley 1999a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format at 5-min resolution.Navin Ramankutty and Jonathan Foley, of the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, developed a global, spatially explicit data set of reconstructed historical croplands from 1700 to 1992. The method for historical reconstruction used a simple algorithm that linked contemporary satellite data and historical cropland inventory data. A spatially explicit croplands data set for 1992 was first derived by calibrating a satellite-derived land cover classification data set against cropland inventory data for 1992. This derived data set was then used within a simple land cover change model, along with historical cropland inventory data, to derive spatially explicit maps of historical croplands. The global data set was restricted to a representation of permanent croplands (i.e., excluding shifting cultivation), which follows the Food and Agriculture Organization (FAO) definition of arable lands and permanent crops. Data values represent fraction of grid cell in croplands.Data for the LBA study area are available for the years 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, and 1992. Although the global croplands data set contains data representing croplands since 1700, essentially no croplands were in the LBA study area until 1900. Data from previous years were excluded at the suggestion of the data originator.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/historical_croplands/comp/uwcrop_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.",
         "license": "proprietary"
     },
-    {
-        "id": "history-of-wetlands-in-switzerland-since-1850_1.0",
-        "title": "History of wetlands in Switzerland since 1850",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815260-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815260-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/history-of-wetlands-in-switzerland-since-1850_1.0",
-        "description": "Naturally, large parts of the Swiss Plateau are characterised by wetlands  and meandering rivers. That this is no longer the case today is the result  of centuries of efforts to obtain dry land. But how did this process take  place? What were the relevant actors and what were their motivations? And  what can be said about the ecological consequences of this development?    In a research project on the history of wetlands in Switzerland since 1700,  we conducted (a) a historical analysis of the development of land use in  wetlands and the actors involved, (b) a historical-cartographic  reconstruction of wetland extent since 1850 and (c) an evaluation of  ecological effects of changes in wetlands on various organisms groups.    The series of GIS layers on wetland history stem from the second part of  the project. The area reconstruction is based on digitized and homogenized  signatures from national map series, as they have been available since  about 1850. Details about the digitalization process and the homogenization  procedures applied (\"Rekonstruktionen\") are included in Stuber & B\u00fcrgi  2019.    __Book Citation:__   > Stuber M, B\u00fcrgi M (2019) Vom \u00aberoberten Land\u00bb zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. \"Bristol Schriftenreihe\", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.",
-        "license": "proprietary"
-    },
     {
         "id": "hiwat_1",
         "title": "High-Impact Weather Assessment Toolkit (HIWAT)",
@@ -232998,19 +227330,6 @@
         "description": "Aerial photography (Linhof) of penguin colonies was acquired over the Holme Bay (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Data conforms to SCAR Feature Catalogue which can be searched (refer to link below).",
         "license": "proprietary"
     },
-    {
-        "id": "how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0",
-        "title": "How do stability corrections perform in the stable boundary layer over snow?",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815275-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815275-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0",
-        "description": "We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.  The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.   Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.  The selected datasets and corresponding test sites are briefly introduced below:    Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in St\u00f6ssel et al. (2010) and Mott et al. (2011).    Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.     Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).      Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 \u00b0N, 38.8 \u00b0W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).    Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70\u00b042' S, 44\u00b020' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).",
-        "license": "proprietary"
-    },
     {
         "id": "hs3avaps2_2",
         "title": "HURRICANE AND SEVERE STORM SENTINEL (HS3) GLOBAL HAWK ADVANCED VERTICAL ATMOSPHERIC PROFILING SYSTEM (AVAPS) DROPSONDE SYSTEM V2",
@@ -233219,71 +227538,6 @@
         "description": "Satellite image map of Husky Massif, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992.  The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 131-110, 129-110, 129-111). It is projected on a Transverse Mercator projection, and gives some historical text information. The map has both geographical and UTM co-ordinates.",
         "license": "proprietary"
     },
-    {
-        "id": "hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0",
-        "title": "Hydraulic resistance of pores in porous media using DNS of laminar flow",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.5274506, 47.3806378, 8.5274506, 47.3806378",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815300-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815300-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0",
-        "description": "Included are three direct numerical simulations results of Stokes flow in three heterogeneous porous media obtained with OpenFoam simulations. In addition we include three data files that contain point-based extracted pores based on the post-processing as reported in the submitted paper \"Local hydraulic resistance in heterogeneous porous media\" in GRL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0",
-        "title": "Hydro-CH2018 Evolution of stream and lake water temperature under climate change",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815311-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815311-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0",
-        "description": "This report presents past observations and projects the future development of water temperature in Swiss lakes and rivers. Projections are made until the end of the 21st century using the CH2018 climate scenarios. Besides climate change effects on temperature, we also discuss effects on discharge for rivers, and effects on the thermal structure, and specifically the seasonal mixing regime and ice cover of lakes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "hydro-ch2018-reservoirs_1.0",
-        "title": "Hydro-CH2018 reservoirs",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815063-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815063-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hydro-ch2018-reservoirs_1.0",
-        "description": "The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.   Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.    Water demand was estimated for six categories:     - 1) Drinking water (households and tourism),  - 2) industry (second and third sector),   - 3) artificial snow production,   - 4) agriculture (irrigation and livestock feeding),   - 5) ecology (residual flows), and   - 6) hydropower.    Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.  Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.    A detailed description of the simulation and estimation procedures can be found in     *   Brunner, M.I., Bj\u00f6rnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., St\u00e4hli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033\u20131047. https://doi.org/10.1016/j.scitotenv.2019.02.169.    This dataset contains the following information:    1.\tShapefile with the 307 medium-sized Swiss catchments.  2.     Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.  3.\tTextfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.  4.\tTextfiles with the storage volumes per category and catchment.",
-        "license": "proprietary"
-    },
-    {
-        "id": "hydro-ch2018-snow_1.0",
-        "title": "Snow and the water cycle in a changing climate",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815112-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815112-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/hydro-ch2018-snow_1.0",
-        "description": "This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff  currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.  Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0\u00b0 Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.   As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.",
-        "license": "proprietary"
-    },
-    {
-        "id": "hydro-meteorological-simulations-1981-2018_1.0",
-        "title": "Hydro-meteorological simulations for the period 1981-2018 for Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815122-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815122-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hydro-meteorological-simulations-1981-2018_1.0",
-        "description": "The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.",
-        "license": "proprietary"
-    },
     {
         "id": "hydro1k_elevation_xdeg_1007_1",
         "title": "ISLSCP II HYDRO1k Elevation-derived Products",
@@ -233310,32 +227564,6 @@
         "description": "This southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1-degree longitude by 1-degree latitude. The data are provided in both ASCII GRID and binary image file formats.",
         "license": "proprietary"
     },
-    {
-        "id": "hydropot_integral_1.0",
-        "title": "HYDROpot_integral",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815135-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815135-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hydropot_integral_1.0",
-        "description": "## A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)    ## Introduction  The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (\u2264 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.  To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=\u201duse\u201d) and those that are more suitable for protection.    ## Material and method  The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.  A list of data sources can be found here [link to text file]  A list of all parameters can be downloaded here [link to PDF and text files]    ## Dataset description  Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.     ## Changes in the methodology compared to the original method (Hirschi et. al 2013)     * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;  individual cantons such as Zug and St. Gallen have no mapped values according to the modular  concept of the federal government, Valais and Graub\u00fcnden only the main valleys, Ticino and Fribourg not  completely (BAFU 2009).   * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.  centrally available, therefore values in GR were equated with NA.   * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values  equal to NA.   * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under  Federal control and dams serving hydroelectricity (Dam, as of 2013).   * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,  Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.  is affected.   * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.  A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This  evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).  Therefore, an evaluation of the station data is made where critical and possible river segments  with poor quality (median less than 3) exist. Only two longer and one short  sections in Switzerland receive a lower median than 3 for water quality.   * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of  Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number  of sections, a large number of viewpoints in the layer of Swisstopo and  the computation time and computability in ArcGIS, the landscape area is classified as generally viewable  (equal to 1).  16 Method  Additional indicators were added (see Appendix B.2):   * SLS_A21 Dissection   * SLS_A22 Forest areas   * SLS_B03 Hiking trails   * SLS_B10 Residential and vacation homes   * SLS_B11 Tourist infrastructure   * SLS_C01 Landfill   * SLS_C03 Infrastructure   * SLS_C05 Industry   * SLS_C06 Agricultural land  Not to be added, although present to some extent:   * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is  for the whole of Switzerland is limited   * SLS_A15 Legally binding protection and land use planning: the individual river sections are not  clearly designated, i.e. no geodata exist  The following data are also not supplemented, as they are cantonal data:   * SLS_A10 Cantonal nature reserves   * SLS_A16 Forest reserves   * SLS_A17 Cantonal inventories and contractually protected areas",
-        "license": "proprietary"
-    },
-    {
-        "id": "hymenoptera_1.0",
-        "title": "Hymenoptera",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815153-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815153-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/hymenoptera_1.0",
-        "description": "Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.    Data are provided on request to contact person against bilateral agreement.",
-        "license": "proprietary"
-    },
     {
         "id": "iagp_casey_traverse_results_1",
         "title": "Data Report on 1981 Traverses From Casey For IAGP",
@@ -233375,32 +227603,6 @@
         "description": "The Integrated Biosphere Simulator (or IBIS) is designed to be a comprehensive model of the terrestrial biosphere. Tthe model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). IBIS was developed by Center for Sustainability and the Global Environment (SAGE) researchers as a first step toward gaining an improved understanding of global biospheric processes and studying their potential response to human activity  [Foley et al. 1996].  IBIS was constructed to explicitly link land surface and hydrological processes, terrestrial biogeochemical cycles, and vegetation dynamics within a single, physically consistent framework. Furthermore, IBIS was one of a new generation of global biosphere models, termed Dynamic Global Vegetation Models (or DGVMs), that consider transient changes in vegetation composition and structure in response to environmental change. Previous global ecosystem models have typically focused on the equilibrium state of vegetation and could not allow vegetation patterns to change over time. Version 2.5 of IBIS includes several major improvements and additions [Kucharik et al. 2000]. SAGE continues to test the performance of the model, assembling a wide range of continental- and global-scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO2 flux. Using these field data and model results for the contemporary biosphere (1965-1994), their evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO2 flux agreed reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results [Kucharik et al. 2000].",
         "license": "proprietary"
     },
-    {
-        "id": "icbo2020_1.0",
-        "title": "ICBO2020",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.6593323, 46.0692292, 6.5931702, 46.5284458",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815172-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815172-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/icbo2020_1.0",
-        "description": "Ontologies used for the case study in the publication.  Creative Commons (CC) license: CC BY-NC-SA <https://creativecommons.org/licenses/by-nc-sa/3.0/>",
-        "license": "proprietary"
-    },
-    {
-        "id": "ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0",
-        "title": "Ice nucleating particle concentrations active at -15 \u00b0C at Weissfluhjoch",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.806475, 46.832964, 9.806475, 46.832964",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815213-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815213-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0",
-        "description": "This dataset contains number concentrations of ice-nucleating particles active at -15 \u00b0C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title \u201cTowards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling\u201d.",
-        "license": "proprietary"
-    },
     {
         "id": "ice-radar-traverse-mirny-domec-1978_1",
         "title": "Ice Radar Traverse Notes, Mirny-Dome C, 1978",
@@ -233453,19 +227655,6 @@
         "description": "Raw orientations obtained from measurements (and re-measurements) from several ice core boreholes on Law Dome.  Holes include SGP (1979), BHQ (1977,1979), SGF (1974, 1977, 1979), SGB (1979) and BHD (1977, 1979).  These documents have been archived at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "icecube_microct_snow_grainsize_1.0",
-        "title": "IceCube_microCT_Snow_grainsize",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-38.4592, 46.8123672, 9.8472047, 72.5796",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815197-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815197-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/icecube_microct_snow_grainsize_1.0",
-        "description": "The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.",
-        "license": "proprietary"
-    },
     {
         "id": "ikonos_Not provided",
         "title": "IKONOS-2",
@@ -233479,19 +227668,6 @@
         "description": "Since its launch in September 1999, GeoEye's IKONOS satellite has provided a reliable stream of image data since January 2000, which has become the standard for commercial high-resolution satellite data products. With an altitude of 681 km and a revisit time of approximately 3 days, IKONOS produces one-meter panchromatic and four-meter multispectral imagery that can be combined to accommodate a wide range of high-resolution imagery applications.",
         "license": "proprietary"
     },
-    {
-        "id": "illgraben-debris-flow-characteristics-2019-2022_1.0",
-        "title": "Illgraben debris-flow characteristics 2019-2022",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.5961876, 46.2675443, 7.6363564, 46.310011",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082518-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082518-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/illgraben-debris-flow-characteristics-2019-2022_1.0",
-        "description": "List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.  This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.",
-        "license": "proprietary"
-    },
     {
         "id": "imergcpex_1",
         "title": "Integrated Multi-satellitE Retrievals for GPM (IMERG) CPEX V1",
@@ -233505,58 +227681,6 @@
         "description": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) CPEX dataset includes measurements gathered by IMERG during the Convective Processes Experiment (CPEX) field campaign. IMERG combines precipitation estimates from multiple passive microwave (PMW) sensors available in a 30-minute analysis time.  These estimates are retrieved using the Goddard Profiling (GPROF) algorithm that converts PMW brightness temperatures to a precipitation estimate. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. IMERG combines information from the GPM satellite constellation to estimate precipitation over the majority of the Earth's surface. Data are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.",
         "license": "proprietary"
     },
-    {
-        "id": "imis-measuring-network_1.0",
-        "title": "IMIS measuring network",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "6.120228, 45.971755, 10.449316, 47.170837",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082559-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082559-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/imis-measuring-network_1.0",
-        "description": "The Intercantonal Measurement and Information System (IMIS) consists of nearly 200 automatic measuring stations. They are distributed throughout the Swiss Alps and the Jura region and, in most cases, are situated above the tree line, most frequently between 2000 and 3000 m. The stations record the conditions around the clock, in general every 30 minutes.   Most IMIS stations are located in the vicinity of starting zones of potentially destructive avalanches, and provide essential information to local safety officers for public safety in settlements and on the roads. They are also used for snow-hydrological and research purposes and by the avalanche warning service of the SLF.  This dataset comprises data from IMIS snow and wind stations. The snow and wind stations are usually situated close to each other and measure the key weather data required for assessing the avalanche danger.      ## IMIS snow stations    Snow stations are located on wind-protected flat terrain. The snowpack model SNOWPACK calculates the layers and properties of the snowpack throughout the winter for each of the IMIS snow stations.  The following variables are measured or simulated in the standard programme of IMIS snow stations and are available in this dataset:    - Snow depth  - 24-hour new snow (SNOWPACK simulation) - Air and surface temperature  - Wind speed and direction - Relative humidity - Reflected shortwave radiation - Ground temperature - Snow temperature 25 cm, 50 cm and 100 cm above the ground    ## IMIS wind stations    Wind stations are generally situated at higher altitudes on exposed summits or ridges. The following variables are measured in the standard programme of IMIS wind stations and are available in this dataset:    - Wind speed and direction - Air temperature - Relative humidity        __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__.    __To download live data use our [API](https://measurement-api.slf.ch)__.    __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.",
-        "license": "proprietary"
-    },
-    {
-        "id": "impact-des-extremes-sur-les-scieries_1.0",
-        "title": "Impact des \u00e9v\u00e9nements m\u00e9t\u00e9orologiques extr\u00eames sur l'\u00e9conomie foresti\u00e8re suisse: le point de vue des scieries",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815270-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815270-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/impact-des-extremes-sur-les-scieries_1.0",
-        "description": "Extreme events impact on the Swiss forest economy: the sawmill perspective    Supplementary Information  This survey aimed at answering three main questions: (i) What are the Swiss sawmills challenges and actions taken after a large storm/windthrow?, (ii) How do these challenges and actions vary across sawmill size and location?, and (iii) is adaptation from the sawmills to extreme events possible, with regards to wood type, products and required infrastructure?      Informations suppl\u00e9mentaires  Cette enqu\u00eate visait \u00e0 r\u00e9pondre \u00e0 trois questions principales : (i) Quels sont les d\u00e9fis et les mesures prises par les scieries suisses apr\u00e8s une grosse temp\u00eate ou un coup de vent ? (ii) Comment ces d\u00e9fis et ces mesures varient-ils selon la taille et l'emplacement de la scierie ? et (iii) l'adaptation des scieries aux \u00e9v\u00e9nements extr\u00eames est-elle possible, en ce qui concerne le type de bois, les produits et l'infrastructure requise ?  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  Ziel dieser Umfrage war die Beantwortung von drei Hauptfragen: (i) Welche  s sind die Herausforderungen und Massnahmen der Schweizer S\u00e4gewerke nach einem grossen Sturm/Windwurf?, (ii) Wie unterscheiden sich diese Herausforderungen und Massnahmen je nach Gr\u00f6sse und Standort des S\u00e4gewerks? und (iii) Ist eine Anpassung der S\u00e4gewerke an Extremereignisse m\u00f6glich, in Bezug auf Holzart, Produkte und erforderliche Infrastruktur?",
-        "license": "proprietary"
-    },
-    {
-        "id": "impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0",
-        "title": "Impact of non-native tree species in Europe on soil properties and biodiversity: a review",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-10.7226562, 31.8028926, 45.8789063, 67.1358294",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082578-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082578-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0",
-        "description": "Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.",
-        "license": "proprietary"
-    },
-    {
-        "id": "impulse_response_function_script_1.2",
-        "title": "Impulse response functions for nonlinear nonstationary and heterogeneous systems",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815293-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815293-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_response_function_script_1.2",
-        "description": "The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.      Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.",
-        "license": "proprietary"
-    },
     {
         "id": "in2018_v05_1",
         "title": "CTD data of cruise in2018_v05 of the RV Investigator",
@@ -233583,71 +227707,6 @@
         "description": "A collection of inclinometer readings from various ice core boreholes on Law Dome in the late 1970s. Holes recorded include SGF (1974, 1977 and 1979), SBG, SGP (1979) and BHD (1977 and 1979)  These documents have been archived at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "increment-11_1.0",
-        "title": "Increment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815305-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815305-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-11_1.0",
-        "description": "Increase in the volume of stemwood with bark of the trees and shrubs  starting at 12 cm dbh that have survived between two inventories and of the losses (modelled for the half period), plus the volume of the gains. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "increment_star-162_1.0",
-        "title": "Increment*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815315-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815315-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_star-162_1.0",
-        "description": "Increase in the volume of stemwood with bark of the surviving trees and shrubs  starting at 12 cm dbh between two inventories and the losses (modelled for the half period), plus the volume of gains.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0",
-        "title": "Individual tree TLS point clouds for tree volume estimation",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.6011658, 47.2560873, 8.6585999, 47.609095",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082133-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082133-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-tree-tls-point-clouds-for-tree-volume-estimation_1.0",
-        "description": "## Dataset  This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.   The data was acquired on two sites (47.42\u00b0N 8.49\u00b0E and 47.504\u00b0N, 7.78\u00b0E), both of which were managed mixed temperate forest stands.  Individual trees were semi-automatically segmented from the co-registered TLS point clouds.    ## Background  Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.  Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.  In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).  Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.",
-        "license": "proprietary"
-    },
-    {
-        "id": "induced-rockfall-dataset-chant-sura_1.0",
-        "title": "Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Fl\u00fcelapass, Grisons, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.9632928, 46.7401819, 9.9743649, 46.7500628",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815108-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815108-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-rockfall-dataset-chant-sura_1.0",
-        "description": "The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.     Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:    # Resources (individual zip-archives)    __ExperimentalRuns__:  Archive with all available StoneNode data streams and its respective figure (.mat files)    __Input__: Archive containing folders     * GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,   * UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and   * VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times    __EOTA__: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.    __Output__: Reconstruced trajectory information for all 82 reconstructed trajectories    __Video__: available video streams for all runs      ## Further information   Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:    A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/  P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/    Caviezel, A., Demmel, S. E., Ringenbach, A., B\u00fchler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199\u2013210, https://doi.org/10.5194/esurf-7-199-2019, 2019",
-        "license": "proprietary"
-    },
-    {
-        "id": "inishell-2-0-4_2.0.4",
-        "title": "Inishell-2.0.4",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815121-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815121-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-2-0-4_2.0.4",
-        "description": "This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge).",
-        "license": "proprietary"
-    },
     {
         "id": "inpe_CPTEC_GLOBAl_FORECAST_Not provided",
         "title": "Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC)",
@@ -233661,32 +227720,6 @@
         "description": "CPTEC offers global model analysis and forecast images for twelve         meteorological parameters. Forecast time steps range from the initial         analysis each day out to six days. The user may choose forecasts from         the most recent forecast run back to the previous 36 hours.                  Parameters Forecasted:                  Mean Sea Level Pressure         Temperature at 1000 hPa         Relative Humidity at 925 hPa, 850 hPa         Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa         Velocity Potential at 925 hPa, 200 hPa         Stream Function at 925 hPa, 200 hPa         500/1000 hPa Thickness         Advection of Temperature at 925 hPa, 850 hPa, 500 hPa         Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa         Convergence of Humidity Flux at 925 hPa, 850 hPa         Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa         Total Precipitation Last 24 Hours                  All forecast images can be obtained via World Wide Web from the CPTEC         Home Page.         Link to: \"http://www.cptec.inpe.br/\"",
         "license": "proprietary"
     },
-    {
-        "id": "input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0",
-        "title": "Input data for break point detection of Swiss snow depth time series",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0",
-        "description": "Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.    ## Columns   Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero",
-        "license": "proprietary"
-    },
-    {
-        "id": "input-data-for-impact-assessment-of-homogenised-snow-series_1.0",
-        "title": "Input data for impact assessment of homogenised snow series",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-data-for-impact-assessment-of-homogenised-snow-series_1.0",
-        "description": "# Input data for the following research article: Impact assessment of homogenised snow depth series on trends  The data consists of separate output files from the following homogenisation methods:  * Climatol  * HOMER  * interpQM  The variable is seasonal mean snow depth (HSavg)    plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude)",
-        "license": "proprietary"
-    },
     {
         "id": "insects_subsaharanAfrica_Not provided",
         "title": "A Checklist of the Insects of Subsaharan Africa",
@@ -233713,58 +227746,6 @@
         "description": "The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia        has four laboratories.  Regular trawl surveys are done by the Laboratory of        Marine Living Resources to assess the exploitable resource stocks.              This dataset consists of 7664 records of 90 families.",
         "license": "proprietary"
     },
-    {
-        "id": "intercomparison-of-photogrammetric-platforms_1.0",
-        "title": "Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.7544861, 46.6485877, 10.0428772, 46.844319",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/intercomparison-of-photogrammetric-platforms_1.0",
-        "description": "This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper \"Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping\".    __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pl\u00e9iades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles.    When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.",
-        "license": "proprietary"
-    },
-    {
-        "id": "interview-guide-and-transcripts_1.0",
-        "title": "Interview guide and transcripts (CONCUR Aim 2 on Governance)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-guide-and-transcripts_1.0",
-        "description": "This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed.",
-        "license": "proprietary"
-    },
-    {
-        "id": "intratrait_1.0",
-        "title": "intratrait",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "6.02, -46.64, 178.52, 53.46",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082491-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082491-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_1.0",
-        "description": "This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species\u2019 height ratio, its height relative to its nearest neighbour.  The following traits and parameters were measured and are available in this data set:  As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b \u03a0). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.   For more details see Rixen et al. 2022, Journal of Ecology.",
-        "license": "proprietary"
-    },
-    {
-        "id": "inventaire-forestier-national-suisse-2009-2017_1.0",
-        "title": "Inventaire forestier national suisse. R\u00e9sultats du quatri\u00e8me inventaire 2009-2017",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815279-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815279-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-forestier-national-suisse-2009-2017_1.0",
-        "description": "Swiss National Forest Inventory. Results of the fourth survey 2009\u20132017.  The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings.  Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland        Inventaire forestier national suisse. R\u00e9sultats du quatri\u00e8me inventaire 2009-2017.  Les relev\u00e9s du quatri\u00e8me inventaire forestier national suisse (IFN) ont eu lieu entre 2009 et 2017, en moyenne huit ans apr\u00e8s le troisi\u00e8me inventaire. Les r\u00e9sultats sur l\u2019\u00e9tat et l\u2019\u00e9volution de la for\u00eat suisse sont pr\u00e9sent\u00e9s et expliqu\u00e9s en d\u00e9tail. Le rapport est structur\u00e9 th\u00e9matiquement selon les crit\u00e8res et indicateurs europ\u00e9ens pour la gestion durable des for\u00eats\u2009: ressources foresti\u00e8res, sant\u00e9 et vitalit\u00e9, production de bois, diversit\u00e9 biologique, for\u00eat protectrice et socio-\u00e9conomie. L\u2019ouvrage s\u2019ach\u00e8ve par un bilan de la durabilit\u00e9 bas\u00e9 sur les r\u00e9sultats de l\u2019IFN.  Mots-cl\u00e9s\u2009: surface foresti\u00e8re, volume de bois, accroissement, exploitation, structure de la for\u00eat, \u00e9tat de la for\u00eat, production de bois, biodiversit\u00e9, for\u00eat protectrice, r\u00e9cr\u00e9ation, durabilit\u00e9, r\u00e9sultats de l\u2019inventaire forestier national, Suisse       Content license: All rights reserved. Copyright \u00a9 2020 by WSL, Birmensdorf.",
-        "license": "proprietary"
-    },
     {
         "id": "islscp2_soils_1deg_1004_1",
         "title": "ISLSCP II Global Gridded Soil Characteristics",
@@ -233778,19 +227759,6 @@
         "description": "This data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS). The data in this data set were produced by the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) staff from data obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/). See the related data sets section below. Two-dimensional gridded maps of selected soil parameters, including soil texture, at a 1.0 by 1.0 degree spatial resolution and for two soil depths are provided. All data layers have been adjusted to match the ISLSCP II land/water mask. There are 36 data files with this data set.",
         "license": "proprietary"
     },
-    {
-        "id": "isotope-lab_1.0",
-        "title": "Stable Isotope Research Lab WSL",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.45634, 47.360992, 8.45634, 47.360992",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815291-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815291-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lab_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg \"Isotope Laboratory WSL\")    The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL.",
-        "license": "proprietary"
-    },
     {
         "id": "isslis_v2_fin_2",
         "title": "Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data V2",
@@ -233869,19 +227837,6 @@
         "description": "Satellite image map of Jetty Peninsula, Mac. Robertson Land, Antarctica. This map is part (d) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991.  The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot tracks, glaciers/ice shelves, and stations/bases. The map has only geographical co-ordinates.",
         "license": "proprietary"
     },
-    {
-        "id": "jfetzer-phosphatase-leaching_1.0",
-        "title": "Phosphatase leaching",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "7.8588858, 49.9488636, 13.7036124, 53.3024328",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815303-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815303-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/jfetzer-phosphatase-leaching_1.0",
-        "description": "Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate.   Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany   of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest.   Soil samples were taken in July 2019. For methods see publication.",
-        "license": "proprietary"
-    },
     {
         "id": "jornada_albedo_667_1",
         "title": "PROVE Surface albedo of Jornada Experimental Range, New Mexico, 1997",
@@ -234246,19 +228201,6 @@
         "description": "The KIND NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.",
         "license": "proprietary"
     },
-    {
-        "id": "kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0",
-        "title": "Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.2040405, 47.5223016, 8.2610321, 47.5371377",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815314-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815314-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0",
-        "description": "The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.",
-        "license": "proprietary"
-    },
     {
         "id": "kiwximpacts_1",
         "title": "KIWX NEXRAD IMPACTS V1",
@@ -234623,45 +228565,6 @@
         "description": "The KVWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.",
         "license": "proprietary"
     },
-    {
-        "id": "l-band-davos-laret_1.0",
-        "title": "L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8748744, 46.8475483, 9.8748744, 46.8475483",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815292-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815292-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-band-davos-laret_1.0",
-        "description": "Dataset from the publication \"L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory\", under review in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. volume and issue TBD.     Dataset specifics are described in the publication.",
-        "license": "proprietary"
-    },
-    {
-        "id": "labchemistrymetamorphism_1.0",
-        "title": "Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.2224941, 47.5363844, 8.2224941, 47.5363844",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815065-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815065-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/labchemistrymetamorphism_1.0",
-        "description": "Earth\u2019s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "labes_1.0",
-        "title": "LABES 2 Indicators of the Swiss Landscape Monitoring Program",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082114-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082114-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_1.0",
-        "description": "The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception.   This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES.",
-        "license": "proprietary"
-    },
     {
         "id": "lai_45_1",
         "title": "Leaf Area Index Data (OTTER)",
@@ -234675,19 +228578,6 @@
         "description": "LAI estimates computed from unweighted openness by the CANOPY program from digitized canopy photographs",
         "license": "proprietary"
     },
-    {
-        "id": "lake_cc_scenarios_ch2018_1.0",
-        "title": "Lake climate change scenarios CH2018",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082136-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082136-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_cc_scenarios_ch2018_1.0",
-        "description": "The dataset \"Lake_climate_change_scenarios_CH2018\" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled \"Lower summer lake levels in regulated perialpine lakes, caused by climate change,\" authored by Wechsler et al. in 2023.    The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun).    In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes.",
-        "license": "proprietary"
-    },
     {
         "id": "lake_erie_aug_2014_0",
         "title": "2014 Lake Erie measurements",
@@ -234714,19 +228604,6 @@
         "description": "This dataset is the GIS data used for the map 'Geology of the Lambert Glacier - Prydz Bay Region, East Antarctica'  published by the Australian Geological Survey Organisation in January 1998.  The data is in three formats: ArcInfo interchange, ArcInfo coverage and shapefile.  A document is included with further information about the data. The map is available from a URL in this metadata record.",
         "license": "proprietary"
     },
-    {
-        "id": "land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0",
-        "title": "Land use/cover dynamics in Austin metropolitan area since 1992",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-97.7014167, 30.3732703, -97.7014167, 30.3732703",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815150-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815150-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0",
-        "description": "The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006\u20132011 and 2011\u20132016. The study also aims to understand to what extent the changes in the built environment (in terms of \u2018efficient growth\u2019 versus \u2018inefficient growth\u2019) from the 1990s to 2016 in the Austin metropolitan area corresponded with \u2018compact and efficient growth\u2019 planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 \u00d7 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.",
-        "license": "proprietary"
-    },
     {
         "id": "land_cover_data-1km_627_1",
         "title": "SAFARI 2000 Land Cover from AVHRR, 1-km, 1992-1993 (Hansen et al.)",
@@ -234870,84 +228747,6 @@
         "description": "Landsat Collection 2 Level-2 Science Products (https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected surface reflectance (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and surface temperature (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present. This dataset represents the Brazilian archive of Level-2 data from Landsat Collection 2 (https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the Thematic Mapper (https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the Enhanced Thematic Mapper (https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and Operatational Land Imager (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and Thermal Infrared Sensor (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF (https://www.cogeo.org/) format.",
         "license": "proprietary"
     },
-    {
-        "id": "landscape-technology-fit-public-evaluation_1.0",
-        "title": "Hybrid choice modelling dataset for the effects of landscape-technology fit on public evaluations",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815133-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815133-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaGlnaCByZXNvbHV0aW9uIG1vbnRobHkgcHJlY2lwaXRhdGlvbiBhbmQgdGVtcGVyYXR1cmUgdGltZXNlcmllcyBmb3IgdGhlIHBlcmlvZCAyMDA2LTIxMDBcIixcIkVOVklEQVRcIixcImNoZWxzYV9jbWlwNV90c1wiLFwiMS4wXCIsMjc4OTgxNDgxMSw3XSIsInVtbSI6IltcImhpZ2ggcmVzb2x1dGlvbiBtb250aGx5IHByZWNpcGl0YXRpb24gYW5kIHRlbXBlcmF0dXJlIHRpbWVzZXJpZXMgZm9yIHRoZSBwZXJpb2QgMjAwNi0yMTAwXCIsXCJFTlZJREFUXCIsXCJjaGVsc2FfY21pcDVfdHNcIixcIjEuMFwiLDI3ODk4MTQ4MTEsN10ifQ%3D%3D/landscape-technology-fit-public-evaluation_1.0",
-        "description": "We present stated preference data based on a national representative Swiss online panel survey for the preference of renewable energy infrastructure in landscapes. The data was collected between November 2018 to March 2019 using an online questionnaire and resulted on 1026 responses. The online questionnaire consisted of two main parts \u2013 (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure, including the \u201cfit\u201d of landscape/REI combinations and (2) online choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific renewable energy infrastructures, we also asked them to evaluate the fitting of seven different Swiss landscapes (near natural alpine areas, northern alps, touristic alpine areas, agricultural plateau, urban plateau, jura ridges, urban alpine valley) with five different REI (wind, PV ground, PV roof, power lines) combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two \u201cenergy system transformation\u201d options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes (landscape, wind energy infrastructure, photovoltaic energy infrastructure, high voltage overhead power line infrastructure) with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modelling and analysis was n=844 (12660 choice observations). An analysis of the hybrid choice model and further insights are presented in the article \u201cHow landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.\u201d",
-        "license": "proprietary"
-    },
-    {
-        "id": "landscape_1.0",
-        "title": "Landscape in contemporary strategic spatial plans of European Urban regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-18.4570312, 34.9264749, 30.7617187, 67.7614772",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815118-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815118-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_1.0",
-        "description": "The present dataset is part of the published scientific paper Hersperger, A.M., B\u00fcrgi, M., Wende, W., Bac\u0103u, S. and Gr\u0103dinaru, S.R., 2020. Does landscape play a role in strategic spatial planning of European urban regions?. Landscape and Urban Planning, 194, p.103702.    The goal of this research was to assess the role of landscape in contemporary strategic spatial planning.  In order to assess the role of \u201clandscape\u201d in the strategic spatial plans, we focused on how plans took advantage of landscape\u2019s integrative power, how plans are based on knowledge on functioning of landscape systems, and how plans show the contribution of landscapes to human well-being. For each aspect, a number of items (detailed in Table 1 of the paper) were selected to assist the assessment.   This study is based on content analysis of the strategic spatial plans of 18 European urban regions. The strategic spatial plans were retrieved from the planning authorities\u2019 websites.  The cases study regions as well as the analyzed strategic spatial plans are presented in Table 2 of the paper.   The authors developed a protocol containing  28 items, out of which 16 were directly derived from information presented in Table 1. As a result, we provide the following outputs:  \u2022\tProtocol_items.docx \u2013 freely available - Detailed description of all the protocol items used to conduct the analysis.   \u2022\tCoding results.xlsx \u2013 available on request - Results of the coding procedure. Data were used to create Figures 2, 3, 4, 5, 6 and to qualitatively present the results in the research paper.",
-        "license": "proprietary"
-    },
-    {
-        "id": "large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0",
-        "title": "Large Scale Hazard Indication Simulations for avalanches, canton of Grisons",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.6511167, 46.1701851, 10.4920496, 47.0651481",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815169-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815169-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0",
-        "description": "We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account.    The details can be found in this publication:    B\u00fchler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., K\u00fchne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.",
-        "license": "proprietary"
-    },
-    {
-        "id": "large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0",
-        "title": "Large-scale risk assessment on snow avalanche hazard in alpine regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.5205841, 46.6437796, 8.6819458, 46.8977391",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082375-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082375-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0",
-        "description": "Potential release files and the artificial RAMMS avalanche simulation output files as well as exposure geodataframe for the case study region of Ortner et al. 2022. Furthermore, all the necessary files to run the risk model Climada Avalnache which code is located at https://github.com/CLIMADA-project/climada_papers.",
-        "license": "proprietary"
-    },
-    {
-        "id": "large-scale-urban-development-projects-in-european-urban-regions_1.0",
-        "title": "lsUDPS Large-scale urban development projects in European urban regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-15.1171875, 38.2726885, 19.1601563, 62.554207",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815221-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815221-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/large-scale-urban-development-projects-in-european-urban-regions_1.0",
-        "description": "Table of Content: 1. General context of the data set \"lsUDPs\" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation    1. General context of the data set \"lsUDPs\"  The data set \"lsUDPs\" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Gr\u0103dinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96\u2013105. https://doi.org/10.1016/j.cities.2019.05.032 ).    2. Background and aims of the study using the data set lsUDPs  As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.  Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).  Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.  Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.  The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or \u201cconcordance\u201d, we mean that strategic projects are formulated and implemented as part of the strategic planning process (\u201chigh concordance\u201d), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (\u201cmoderate concordance\u201d). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.  We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly \u201ccause\u201d) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).  The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?  The conditions (\u201cindependent variables\u201d) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.  The outcome (\u201cdependent variable\u201d) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans     3. The data set lsUDPs          3.1 Selection of cases and data collection  To generate the current data set on large-scale urban development projects in European urban regions (data set \"lsUDPs\"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.  In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s\u20132010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases \u201cbcn_airport80-90\u201d and \u201cbcn_airport00-16\u201d; realisation of Lyon Part-Dieu, cases \u201clyo_partdieu70-90\u201d and \u201clyo_partdieu00-16\u201d; MediaCityUK, cases \u201cman_salfordquays80-00\u201d and \u201cman_mediacityuk00-16\u201d). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.           3.2 The data set lsUDPs: Data management and operationalisation  Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled \u201csynthetic case descriptions\u201d (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees\u2019 accounts and to facilitate completion of the \u201ccalibration table\u201d for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.  For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called \u201ccalibration\u201d; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as \u201cfundamentally interpretative tools\u201d that \u201coperationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence\u201d (Ragin 2000:162, in \u201cFuzzy-set Social Science\u201d. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to \u201cquantify\u201d into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).   The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.",
-        "license": "proprietary"
-    },
-    {
-        "id": "large-wood-event-database_1.0",
-        "title": "Large wood event database",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815250-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815250-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-wood-event-database_1.0",
-        "description": "In the context of the WoodFlow project (https://woodflow.wsl.ch), an extensive database was developed which documents recruited and transported quantities of large wood (woody debris) together with the associated catchment and flood-specific parameters. Transported large wood volumes were related to catchment area, forest cover, stream length, peak discharge, runoff volume, sediment load, and precipitation. The dataset covers flood events mostly from Switzerland, but also from other alpine catchments in Germany, Italy France and Japan.",
-        "license": "proprietary"
-    },
     {
         "id": "lars_christ_sat_1",
         "title": "Lars Christensen Coast Satellite Image Map 1:500 000",
@@ -235039,32 +228838,6 @@
         "description": "Annotated large format maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica. The compilation was done by Ewan McIvor of the Australian Antarctic Division and based on discussions with scientists Jim Burgess and Andy Spate. Included are locations and notes relating to: 1 walking and vehicular routes; 2 helicopter landing sites; 3 a tide gauge; 4 a fuel line; 5 a grave site; 6 a long term micro erosion monitoring site established in 1990 by Burgess and Spate; 7 two ice caves; and 8 a pliocene deposit.",
         "license": "proprietary"
     },
-    {
-        "id": "larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0",
-        "title": "Larval food composition of four wild bee species in five European cities",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "0.2197266, 46.890732, 28.3886719, 59.0864909",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815269-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815269-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0",
-        "description": "Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.",
-        "license": "proprietary"
-    },
-    {
-        "id": "latent-reserves-in-the-swiss-nfi_1.0",
-        "title": "'Latent reserves' within the Swiss NFI",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/latent-reserves-in-the-swiss-nfi_1.0",
-        "description": "The files refer to the data used in Portier et al. \"\u2018Latent reserves\u2019: a hidden treasure in National Forest Inventories\" (2020) *Journal of Ecology*.           **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered \u2018latent reserves\u2019, criteria were defined based on the information available in the Swiss NFI database:           * Shrub forests were excluded.  * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested.  * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Kl\u00f6tzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994).  * Plots had to originate from natural regeneration.   * Intensive livestock grazing must never have occurred on the plots.          The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).    ****    The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper.     The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.",
-        "license": "proprietary"
-    },
     {
         "id": "law_dome_1977_1",
         "title": "Law Dome Field Logs And Strain Grid Results, 1977",
@@ -235533,32 +229306,6 @@
         "description": " The Legal Amazon of Brazil is defined by law to include the states of Acre, Amapa, Amazonas, Para, Rondonia, Roraima, Mato Grosso, Maranhao, and Tocantins [Fundacao Instituto Brasileiro de Geografia e Estatistica (IBGE) 1991]. This is the definition used in generating the Legal Amazon mask. The 8-km Legal Amazon mask was generated by Christopher Potter at the Ecosystem Science and Technology Branch of the Earth Science Division at NASA Ames Research Center (Potter and Brooks-Genovese 1999). The mask was generated from the Digital Chart of the World available from Environmental Systems Research Institute, Inc. (ESRI). The mask is available in ASCII GRID format. The README file accompanying the mask has more information regarding data format. More information can be found at ftp://daac.ornl.gov/data/lba/human_dimensions/legal_amazon_mask/comp/legamazon_readme.pdf.",
         "license": "proprietary"
     },
-    {
-        "id": "length_of_forest_edge-8_1.0",
-        "title": "Length of forest edge",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815302-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815302-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_of_forest_edge-8_1.0",
-        "description": "Length of the forest edge calculated on the basis of the forest boundary lines determined in the aerial photo.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "length_of_forest_roads-78_1.0",
-        "title": "Length of forest roads",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815312-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815312-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_of_forest_roads-78_1.0",
-        "description": "The length of forest roads corresponds to the length of the NFI forest roads. This length was calculated according to the method of the specific NFI concerned.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "level_1_annual_co2_895_1",
         "title": "TransCom 3: Annual Mean CO2 Flux Estimates from Atmospheric Inversions (Level 1)",
@@ -235611,32 +229358,6 @@
         "description": "The Lambert Glacier Basin Traverse program ran from the summer of 1989-90 to the summer of 1994-95. The aim of the program was to take multi-year measurements on the dynamics of the ice-sheet draining into the Lambert Glacier, from around the 2500m ice surface elevation contour. These measurements were then used in mass balance calculations for the whole Lambert-Amery system.  Annual reports were written during this program, detailing the activities and science carried out, equipment and personnel used, travel logs, fuel consumption, and problems encountered. These reports have been archived at the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "lidar-davos-wolfgang_1.0",
-        "title": "LIDAR Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815358-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815358-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-davos-wolfgang_1.0",
-        "description": "A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lidar-wind-profiler-data_1.0",
-        "title": "Wind LIDAR Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815339-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815339-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/lidar-wind-profiler-data_1.0",
-        "description": "Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.",
-        "license": "proprietary"
-    },
     {
         "id": "lidar_6",
         "title": "Lidar Studies of Atmospheric Structure, Dynamics and Climatology",
@@ -235650,19 +229371,6 @@
         "description": "The lidar profiles density, temperature, wind velocity and aerosol loading from the lower troposphere to the upper mesosphere, depending on operating mode.  Two main measurement techniques are employed. Firstly, traditional Rayleigh backscatter analysis yields temperature profiles above the top of the stratospheric aerosol layer (greater than about 27km altitude). The temperatures are obtained from lidar-derived density profiles, calibrated with in-situ radiosonde data below 40km altitude, using the standard hydrostatically-constrained perfect gas law model. When available, hydroxyl-layer temperatures obtained locally by a Czerny-Turner spectrograph are used as an upper boundary condition on the temperature retrieval algorithm. Rayleigh backscatter can be detected from altitudes as high as 100km, although useful temperatures are normally limited to below 80km. Observations of rotational-vibrational Raman backscatter from molecular oxygen or nitrogen are used to extend the temperature profiles into the lower stratosphere and upper troposphere. Profiles of aerosol-loading are derived from standard scattering-ratio analysis, allowing identification of clouds in the upper troposphere, stratosphere (Polar Stratospheric Clouds) and mesosphere (Polar Mesospheric Clouds).  Secondly, spectral scans of laser backscatter are obtained with a high-resolution Fabry-Perot spectrometer. These are used to infer the line-of-sight wind speed and temperature by using the Doppler effect. Observations along 'cardinal point' lines-of-sight provide information on wind direction. In general, Doppler measurements are restricted to altitudes below about 70km based on signal detection considerations. Some information on aerosol loading is obtained from analysis of the spectral properties of the backscatter.  The lidar is capable of both day and night measurements covering a large altitude range, and in so doing will provide information for the study of climate change and a range of atmospheric phenomena on a variety of spatial and temporal scales.    Taken from the 2008-2009 Progress Report: Progress against objectives: At Davis, lidar measurements of temperature and aerosol properties were acquired for the troposphere, stratosphere and mesosphere. Additionally, ozone data were acquired for the troposphere and lower stratosphere.  Ongoing analyses of these data is providing new information on the composition, dynamics and climate of the polar atmosphere. During the reporting period, continued progress was achieved in international collaborative studies of Polar Stratospheric Cloud microphysics as part of the International Polar Year, and measurements of Polar Mesospheric Clouds for the Aeronomy of Ice in the Mesosphere (AIM) satellite mission. Both of these activities contribute to all 4 goals of the project.   Taken from the 2009-2010 Progress Report: Progress against objectives: New data were obtained for the study of the long-term climate in the Antarctic middle atmosphere (5-95km altitude), and atmospheric phenomena under extreme physical conditions. The highlights were: (1) Detailed measurements of ice clouds in the summer mesopause region for validation of climate models. (2) Further measurements of the properties and dynamics of Polar Stratospheric Clouds for research aimed at improving projections of the recovery of the Ozone Hole. (3) Initial measurements for a new study of the interactions between the troposphere and stratosphere which is aimed at improved knowledge of climate processes in the tropopause region.",
         "license": "proprietary"
     },
-    {
-        "id": "lidar_forest_myotis-myotis_1.0",
-        "title": "LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815310-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815310-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_forest_myotis-myotis_1.0",
-        "description": "Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.",
-        "license": "proprietary"
-    },
     {
         "id": "lima_Not provided",
         "title": "Landsat Image Mosaic of Antarctica (LIMA)",
@@ -235676,19 +229384,6 @@
         "description": "A team of scientists from the U.S. Geological Survey, the British Antarctic Survey, and the National Aeronautics and Space Administration, with funding from the National Science Foundation, created LIMA in support of the International Polar Year (IPY; 2007\u201308). ",
         "license": "proprietary"
     },
-    {
-        "id": "linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0",
-        "title": "Linked water discharge, bedload transport and bedrock erosion data set in 1minute resolution",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.70875, 47.0449764, 8.70875, 47.0449764",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815360-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815360-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0",
-        "description": "This data set includes synchronized and independently measured water discharge, bedload transport and at-a-point bedrock erosion data in 1 minute resolution and over more than 1.5 years from the Erlenbach stream hydrological observatory, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. These measurements are of high accuracy, which have been assessed in Beer, A.R. et al. 2015. Earth Surf. Proc., 40, 530-541. doi: 10.1002/esp.3652. For the artificial bedrock (a slab of weak concrete, fixed flush with the streambed) 6 additional consecutive spatial elevation data sets of 1 mm resolution have been surveyed that allow the local continuous erosion measurements to be extended to the patch scale.    This unique data set has been used to validate and calibrate bedrock erosion models for the process to intermediate scales of time (and space), whose performance then was assessed over extended time (up to bicentennial floods), based on available longer data sets of linked discharge and bedload transport (see related datasets).",
-        "license": "proprietary"
-    },
     {
         "id": "lipimpacts_2",
         "title": "Lightning Instrument Package (LIP) IMPACTS V2",
@@ -235793,19 +229488,6 @@
         "description": "The LIS 0.1 Degree Very High Resolution Gridded Lightning Seasonal Climatology (VHRSC) dataset consists of gridded seasonal climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.",
         "license": "proprietary"
     },
-    {
-        "id": "literature-data-of-sound-speed-in-snow_1.0",
-        "title": "Literature data of sound speed in snow",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815412-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815412-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/literature-data-of-sound-speed-in-snow_1.0",
-        "description": "This dataset contains literature data for snow density and frequency dependency of speed of sound waves in snow. The data were either available as tabular data in the original publications or were digitized from plots contained in the original publications. The data were originally collected and used for first figure in Capelli et al. (2016) .",
-        "license": "proprietary"
-    },
     {
         "id": "litter_decomp_651_1",
         "title": "Effects of Elevated Carbon Dioxide on Litter Chemistry and Decomposition",
@@ -235949,19 +229631,6 @@
         "description": "The LIS/OTD 2.5 Degree Low Resolution Time Series (LRTS) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRTS dataset include flash rate time series data in MP4 format.",
         "license": "proprietary"
     },
-    {
-        "id": "long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0",
-        "title": "Long-term recovery of above-and belowground interactions in restored grasslands",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815634-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815634-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0",
-        "description": "This dataset contains all data, on which the following publication below is based.    __Paper Citation:__    _Resch, M.C., Sch\u00fctz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_    __Please cite this paper together with the citation for the datafile.__    Study area and experimental design  The study was conducted in and around two nature reserves, Eigental and Altl\u00e4ufe der Glatt, which were located approximately 5 km apart (47\u00b027\u00b4 to 47\u00b029\u00b4 N, 8\u00b037\u00b4 to 8\u00b032\u00b4 E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 \u00b1 0.6 \u00b0C and 990 \u00b1 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).    Collection of plants and selected soil biota data  Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 \u00b0C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 \u03bcL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 \u00b0C for 3 h). Thereafter, 180 \u03bcL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 \u00b0C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 \u00b0C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 \u03bcL of genomic DNA template (ca. 1 ng/\u03bcL) in 25 \u03bcL reactions containing 5 \u03bcL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 \u03bcM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 \u03bcL BSA, and 0.25 \u03bcL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 \u00b0C for 2 min, followed by 35 cycles at 94 \u00b0C for 40 sec, 58 \u00b0C for 40 sec, 72 \u00b0C for 1 min, and a final elongation step at 72 \u00b0C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.  We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 \u00b0C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer\u00b4s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings \u2265 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.    Collection of soil physical and chemical properties  We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 \u00b0C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (\u2264 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.",
-        "license": "proprietary"
-    },
     {
         "id": "long_tryne_bathy_1",
         "title": "Interpolated bathymetry of Long and Tryne Fjords, Vestfold Hills, Antarctica",
@@ -235975,32 +229644,6 @@
         "description": "This GIS dataset is the result of the interpolation of bathymetry from depth measurements made in Long and Tryne Fjords in the Vestfold Hills, Antarctica (see Entry: VH_bathy_99). The Topogrid command within the ArcInfo GIS software, version 8.0.2, was used to do the interpolation. Coastline and spot height (heights above sea level) data, extracted from the Australian Antarctic Data Centre's Vestfold Hills topographic GIS dataset (see Entry: vest_hills_gis), was also used as input data to optimise the interpolation close to the coastline. See related URLs for a map showing the interpolated bathymetry.",
         "license": "proprietary"
     },
-    {
-        "id": "longterm-hydrological-observatory-alptal-central-switzerland_2.0",
-        "title": "Longterm hydrological observatory Alptal (central Switzerland)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "8.7052166, 47.0466744, 8.7052166, 47.0466744",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815465-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815465-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/longterm-hydrological-observatory-alptal-central-switzerland_2.0",
-        "description": "This data set includes 54 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in sub-daily resolution can be provided on demand.  Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and L\u00fcmpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal  .  Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.   Meteorological variables have been measured on a meadow (Erlenh\u00f6he) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and L\u00fcmpenenbach catchments).  Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenh\u00f6he, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in St\u00e4hli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.    Further information on the methods and sensors can be found at https://www.wsl.ch/alptal  .    A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. A second update of the data set (with data from 2020 to 2022) was uploaded in March 2023.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lsa_forest_snow_1.0",
-        "title": "Wintertime UAV-based land surface albedo data over forested environments",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.8704777, 46.8432022, 9.8776875, 46.8462549",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815873-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815873-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/lsa_forest_snow_1.0",
-        "description": "This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper \"Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements\", by Malle et al. (2021, under review). This publication must be cited when using the data.    Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankyl\u00e4). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.",
-        "license": "proprietary"
-    },
     {
         "id": "lsatmssd_435_1",
         "title": "BOREAS Landsat MSS Imagery: Digital Counts",
@@ -236118,19 +229761,6 @@
         "description": "The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.",
         "license": "proprietary"
     },
-    {
-        "id": "luszoning_1.0",
-        "title": "LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "-4.4934082, 38.7600804, 2.7355957, 42.2504783",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816049-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816049-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/luszoning_1.0",
-        "description": "Table of Content: 1. General context of the data set \"LUSzoning\u201d; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.  ###1. __General context of the data set \"LUSzoning\".__   The data set \"LUSzoning\" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.   ###2.\t__Background and aims of the study using the data set \u201cLUSzoning\u201d.__   As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.   Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.  ###3.\t __The data set \u201cLUSzoning\u201d.__   The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named \u201cZaragoza_S2.tif\u201d. Furthermore, a .txt file named \u201cLegend.txt\u201d includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.",
-        "license": "proprietary"
-    },
     {
         "id": "lutzow_holm_bay_bathy_1",
         "title": "Bathymetry of Lutzow-Holm Bay digitised by NIPR from bathymetric chart of Lutzow-Holm Bay",
@@ -236144,487 +229774,6 @@
         "description": "The soundings were digitized from bathymetric chart: Bathymetry of Lutzow-Holm Bukta (Lutzow-Holm Bay) by the Japanese, National Institute of Polar Research (NIPR) from Special Map Series of National Institute of Polar Research No. 4b, 2002 - map number 12852 in the SCAR map catalogue.  These data have been created by the Japanese, but as such no metadata record for the data exists in the Japanese portal of the Antarctic Master Directory.  Australian users of these data should use this metadata record (providing credit to the Japanese), until a Japanese version has been created.",
         "license": "proprietary"
     },
-    {
-        "id": "lwf-alptal-long-term-research-site_1.0",
-        "title": "LWF Alptal long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.713054, 47.04872, 8.713054, 47.04872",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816190-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816190-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-alptal-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg \"LWF Alptal\")    LWF plot Alptal    - Community: Alpthal / canton SZ  - Date of installation: 31 May 1995  - Size of the plot: 0.6 ha  - Altitude: 1149-1170 m  - Mean slope: 23%  - Geology (in German): Nordpenninikum; obere Kreide-unteres Eoz\u00e4n, W\u00e4gitaler Flysch  - Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols  - Woodland association after EK72: 49: Equiseto-Abietetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm  - Number of trees BHD >= 12 cm (2011): 321  - Maximum tree age: Picea abies 180-230 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-beatenberg-long-term-research-site_1.0",
-        "title": "LWF Beatenberg long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.7623374, 46.7003438, 7.7623374, 46.7003438",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816263-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816263-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-beatenberg-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/7310b935-757f-4f27-b202-9f433c9882ab/resource/555b1a19-aff3-40be-9d6d-fea9967d5691/download/lwf_beatenberg.jpg \"LWF Beatenberg\")    LWF plot Beatenberg    - Community: Beatenberg / canton BE  - Date of installation: 25 September 1996  - Size of the plot: 2 ha  - Altitude: 1490-1532 m  - Mean slope: 66%  - Geology (in German): Helvetikum, Terti\u00e4r, Eoz\u00e4n; Hohgantsandstein  - Provisional soil type (WSL) : Gleyic Podzols  - Woodland association after EK72: 57: Sphagno-Piceetum calamagrostietosum villosae  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 46.4 cm  - Number of trees BHD >= 12 cm (2011): 851  - Maximum tree age: Picea abies 190-210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/beatenberg.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-bettlachstock-long-term-research-site_1.0",
-        "title": "LWF Bettlachstock long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.4166536, 47.2251551, 7.4166536, 47.2251551",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816308-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816308-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-bettlachstock-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/8f67193b-12cf-4871-9a9e-750b816e9d10/resource/b05db334-5bf6-42d6-a985-a5253366259d/download/lwf_bettlachstock.jpg \"LWF Bettlachstock\")    LWF Plot Bettlachstock    - Community: Bettlachstock / canton SO  - Date of installation: 6 June 1995  - Size of the plot: 1.28 ha  - Altitude: 1101-1196 m  - Mean slope: 66%  - Geology (in German): Kettenjura; Jura: Dogger, oberer Hauptrogenstein  - Soil types (WSL) : Rendzic Leptosols; Calcaric Cambisols  - Woodland association after EK72: 13 h: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 49.5 cm  - Number of trees BHD >= 12 cm (2011): 632  - Maximum tree age: Fagus sylvatica 170-190 yr - Picea abies 200 yr - Fraxinus excelsior 170 yr - Ulmus glabra 160 yr - Abies alba 190 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/bettlachstock.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-celerina-long-term-research-site_1.0",
-        "title": "LWF Celerina long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8888024, 46.4921451, 9.8888024, 46.4921451",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816326-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816326-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-celerina-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/0c244fe7-886a-4a1c-add5-5f706e995a29/resource/28e97caa-65fc-4396-8bca-860721eddfa2/download/lwf_celerina.jpg \"LWF Celerina\")    LWF Plot Celerina    - Community: Celerina / canton GR  - Date of installation: 3 July 1996  - Size of the plot: 2 ha  - Altitude (m): 1846-1896  - Mean slope: 34%  - Geology (in German): Untergrund: ostalpin; pr\u00e4triadische Tiefengesteine - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie Mor\u00e4ne  - Soil types (WSL): n.d.  - Woodland association after EK72: 59: Larici-Pinetum cembrae  - Main tree species: Pinus cembra  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 48.6 cm  - Number of trees BHD >= 12 cm (2011): 469  - Maximum tree age: Pinus cembra uneven-aged - 210-250 years    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/celerina.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-chironico-long-term-research-site_1.0",
-        "title": "LWF Chironico long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.812172, 46.4468172, 8.812172, 46.4468172",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816346-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816346-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-chironico-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg \"LWF Chironico\")    LWF Plot Chironico    - Community: Chironico / canton TI  - Date of installation: 29 August 1995  - Size of the plot: 2 ha  - Altitude: 1342-1387 m  - Mean slope: 35%  - Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie Mor\u00e4ne, H\u00e4ngeschutt  - Provisional soil type (WSL) : Distric Cambisol  - Woodland association after EK72: 47: Calamagrostio villosae-Abietetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm  - Number of trees BHD >= 12 cm (2011): 750  - Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/chironico.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-isone-long-term-research-site_1.0",
-        "title": "LWF Isone long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.0080555, 46.1248982, 9.0080555, 46.1248982",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816400-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816400-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-isone-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg \"LWF Isone\")    LWF Plot Isone    - Community: Isone / canton TI  - Date of installation: 5 September 1995  - Size of the plot: 2 ha  - Altitude (m): 1181-1259  - Mean slope: 58%  - Geology (in German): Untergrund: S\u00fcdalpin, pr\u00e4permisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfl\u00e4che: Quart\u00e4r; Mor\u00e4ne, H\u00e4ngeschutt-.  - Provisional soil type (WSL) : Humic Cambisol  - Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum  - Main tree species: Fagus sylvatica  - Management system: former coppice  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm  - Number of trees BHD >= 12 cm (2011): 1254  - Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-jussy-long-term-research-site_1.0",
-        "title": "LWF Jussy long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.2908547, 46.2298528, 6.2908547, 46.2298528",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816503-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816503-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-jussy-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/465d852d-af24-415f-9db2-48fe31d6dc20/resource/a39a0e35-efdf-4bef-bab1-4b0532442b34/download/lwf_jussy.jpg \"LWF Jussy\")    LWF Plot Jussy    - Community: Jussy / canton GE  - Date of installation: 31 May 1995  - Size of the plot: 1.99 ha  - Altitude: 496-506 m  - Mean slope: 3%  - Geology (in German): Quart\u00e4r; tonreiche w\u00fcrmeiszeitliche Grundmor\u00e4ne  - Soil types (WSL) : Stagnic Luvisols  - Woodland association after EK72: 35: Galio silvatici-Carpinetum  - Main tree species: Quercus species  - Management system: former coppices w. standards  - Silvicultural system: unmanaged / group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 36.6 cm  - Number of trees BHD >= 12 cm (2011): 1278  - Maximum tree age: Carpinus betulus 60 yr - Populus tremula 60 yr - Quercus petrea 90 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/jussy.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-lageren-long-term-research-site_1.0",
-        "title": "LWF L\u00e4geren long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.3645212, 47.4783603, 8.3645212, 47.4783603",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082992-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082992-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-lageren-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg \"LWF L\u00e4geren\")    This research site is located on the southern slope of the L\u00e4gern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks:     * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))  * [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network  * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html))  * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)    The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)).    LWF Plot L\u00e4geren    - Community: Wettingen / canton AG  - Date of installation: 1.05.2012  - Size of the plot: 1.34 ha  - Altitude: 643 - 718 m  - Mean slope: 37 %  - Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt  - Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina  - Woodland association after Ellenberg and Kl\u00f6tzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum  - Main tree species: fagus sylvatica  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm  - Number of trees BHD >= 12 cm (2011): 503  - Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-lantsch-long-term-research-site_1.0",
-        "title": "LWF Lantsch long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.5646678, 46.6980544, 9.5646678, 46.6980544",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815332-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815332-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/lwf-lantsch-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/9e5d6c82-26ac-4818-9458-1ac7c8574bb0/resource/c26169dc-db58-4e69-bc74-9513dbf7bccc/download/lwf_lantsch.jpg \"LWF Lantsch\")    LWF Plot Lantsch    - Community: Lantsch / canton GR  - Date of installation: 15 September 1997  - Size of the plot: n.d.  - Altitude: 1458-1490 m  - Mean slope: 16%  - Geology (in German): Ostalpin. Geh\u00e4ngeschutt aus mesozoischen Schiefern, Dolomiten und Kalken  - Soil types (WSL) : n.d.  - Woodland association after EK72: 65: Erico-Pinetum silvestris  - Main tree species: Picea abies  - Management system: high forest  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 40.1 cm  - Number of trees BHD >= 12 cm (2011): 709  - Maximum tree age: n.d.    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lantsch.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-lausanne-long-term-research-site_1.0",
-        "title": "LWF Lausanne long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.6580421, 46.5837664, 6.6580421, 46.5837664",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815359-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815359-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-lausanne-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/9f35f697-a226-4719-aefd-b8c96fe5ae7c/resource/d6a48c12-62e6-4c04-9f63-a84d5964bc55/download/lwf_lausanne.jpg \"LWF Lausanne\")    LWF Plot Lausanne    - Community: Lausanne / canton VD  - Date of installation: 5 September 1994  - Size of the plot: 2 ha  - Altitude: 800-814 m  - Mean slope: 7%  - Geology (in German): Untergrund: Terti\u00e4r, Mioz\u00e4n, Burdigalien, obere Meeresmolasse; Sandstein - Oberfl\u00e4che: Quart\u00e4r, W\u00fcrm; w\u00fcrmeiszeitliche Mor\u00e4ne  - Soil types (WSL) : Distric Cambisols  - Woodland association after EK72: 8: Milio-Fagetum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 59.9 cm  - Number of trees BHD >= 12 cm (2011): 650  - Maximum tree age: Abies alba 160-170 yr - Picea abies 160-170 yr - Fagus sylvatica 160-170 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lausanne.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-lens-long-term-research-site_1.0",
-        "title": "LWF Lens long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.4359392, 46.2685558, 7.4359392, 46.2685558",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815410-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815410-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-lens-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/87d3ea5a-8d50-446b-9c33-cbe56057f7d3/resource/bf459948-da5a-4b05-9372-83ca5454aeca/download/lwf_lens.jpg \"LWF Lens\")    LWF Plot Lens    - Community: Lens / canton VS  - Date of installation: 15 March 1996  - Size of the plot: 2 ha  - Altitude: 1033-1093 m  - Mean slope: 75%  - Geology (in German): Untergrund: Penninikum, Ferret-Zone, Trias; sandiger Kalkstein - Oberfl\u00e4che: H\u00e4ngeschutt  - Provisional soil type (WSL): Calcaric Cambisol  - Woodland association after EK72: +- 64: Cytiso-Pinetum silvestris  - Main tree species: Pinus sylvestris  - Management system: high forest  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 31.8 cm  - Number of trees BHD >= 12 cm (2011): 2304  - Maximum tree age:150-170 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lens.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-nationalpark-long-term-research-site_1.0",
-        "title": "LWF Nationalpark long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "10.2300874, 46.662501, 10.2300874, 46.662501",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816200-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816200-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-nationalpark-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/4a0ff376-83d2-4ae4-bf2e-c361eb050778/resource/51e98f2d-5524-4ea6-babb-9df0b9aba8f1/download/lwf_nationalpark.jpg \"LWF Nationalpark\")    LWF Plot Nationalpark    - Community: Zernez / canton GR  - Date of installation: 10 October 1995  - Size of the plot: 2 ha  - Altitude: 1890-1907 m  - Mean slope: 11%  - Geology (in German): Nacheiszeitlicher Schwemmf\u00e4cher; kalkhaltige Mor\u00e4ne, Dolomite, Kalke, Tonschiefer, Rauhwacken  - Provisional soil types (WSL): Rendzic Leptosol  - Woodland association after EK72: 67: Erico-Pinetum montanae  - Main tree species: Pinus mugo  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 23.7 cm  - Number of trees BHD >= 12 cm (2011): 2450  - Maximum tree age: Pinus mugo 210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/nationalpark.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-neunkirch-long-term-research-site_1.0",
-        "title": "LWF Neunkirch long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.535683, 47.6837031, 8.535683, 47.6837031",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816296-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816296-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-neunkirch-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg \"LWF Neunkirch\")      LWF Plot Neunkirch    - Community: Neunkirch / canton SH  - Date of installation: 14 July 1995  - Size of the plot: 2 ha  - Altitude (m): 554-609  - Mean slope: 58%  - Geology (in German): Tafeljura, oberer Malmkalk; Malmh\u00e4ngeschutt  - Soil types (WSL) : Rendzic Leptosols  - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: former coppices w. standards  - Silvicultural system: reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm  - Number of trees BHD >= 12 cm (2011): 442  - Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-novaggio-long-term-research-site_1.0",
-        "title": "LWF Novaggio long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.8341613, 46.0226119, 8.8341613, 46.0226119",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816319-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816319-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-novaggio-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/e87358e9-4beb-487e-af68-66633e9cfc96/resource/91bd053a-61c4-4b20-b0d3-a572c983e647/download/lwf_novaggio.jpg \"LWF Novaggio\")    LWF Plot Novaggio    - Community: Novaggio / canton TI  - Date of installation: 8.3.95  - Size of the plot: 1.5 ha  - Altitude (m): 902-997  - Mean slope: 68%  - Geology (in German): Untergrund: S\u00fcdalpin, pr\u00e4permisches Grundgebirge; Orthogneis, schiefriger Biotitplagioklasgneis - Oberfl\u00e4che: Quart\u00e4r; karbonatfreie w\u00fcrmeiszeitliche Mor\u00e4ne  - Provisional soil type (WSL): Kryptopodzole  - Woodland association after EK72: 42: Phyteumo betonicifoliae-Quercetum castanosum  - Main tree species: Quercus cerris  - Management system: former coppice  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 27.0 cm  - Number of trees BHD >= 12 cm (2011): 1130  - Maximum tree age: Castanea sativa 90 yr- Betula pendula 70 yr - Quercus cerris 70 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/novaggio.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-othmarsingen-long-term-research-site_1.0",
-        "title": "LWF Othmarsingen long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.2267566, 47.3995354, 8.2267566, 47.3995354",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816335-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816335-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-othmarsingen-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/ebb37e73-1280-4a06-81d8-e18bc5d9c1cf/resource/64291bfd-37d4-4f88-b505-81fc85a109c2/download/lwf_othmarsingen.jpg \"LWF Othmarsingen\")    LWF Plot Othmarsingen    - Community: Othmarsingen / canton AG  - Date of installation: 9 September 1994  - Size of the plot: 1 ha  - Altitude (m): 467-500  - Mean slope: 27%  - Soil types (WSL): Stagnic Luvisols, Haplic Luvisols  - Woodland association after EK72: 7: Galio odorati-Fagetum typicum  - Main tree species: Fagus sylvatica  - Management system: former coppices w. standards  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 62.8 cm  - Number of trees BHD >= 12 cm (2011): 167  - Maximum tree age: Fagus sylvatica 120-140 yr - Tilia sp. 120-140 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/othmarsingen.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-pfynwald-long-term-experimental-irrigation-site_1.0",
-        "title": "LWF Pfynwald long-term experimental irrigation site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.6121082, 46.3027884, 7.6121082, 46.3027884",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816348-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816348-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-pfynwald-long-term-experimental-irrigation-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg \"LWF experimental irrigation site Pfynwald\")      As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46\u00b0 18' N, 7\u00b0 36' E, 615 m ASL) offers the best conditions for such measurements.  In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest.  The average temperature here is 9.2\u00b0C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-schanis-long-term-research-site_1.0",
-        "title": "LWF Sch\u00e4nis long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.0670726, 47.1650464, 9.0670726, 47.1650464",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816393-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816393-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-schanis-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg \"LWF Sch\u00e4nis\")    LWF Plot Sch\u00e4nis    - Community: Sch\u00e4nis / canton SG  - Date of installation: 17 September 1997  - Size of the plot: 2 ha  - Altitude: 693-773 m  - Mean slope: 60%  - Geology (in German): Terti\u00e4r. Subalpine Molasse, Oligocaen, Chattien, Kalknagelfluh  - Soil types (WSL) : n.d.  - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 55.8 cm  - Number of trees BHD >= 12 cm (2011): 611  - Maximum tree age: Abies alba130-150 yr - Fraxinus excelsior 130-150 yr - Fagus sylvatica 130-150 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/schaenis.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-seehornwald-davos-long-term-research-site_1.0",
-        "title": "LWF Seehornwald Davos long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8552112, 46.8153458, 9.8552112, 46.8153458",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082636-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082636-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-seehornwald-davos-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/801cdd7e-f5b0-4998-bb8a-bd6d2ae8baa2/resource/f2ef5505-e0ea-493d-8b86-4f27dd556da8/download/lwf_davos.jpg \"LWF Davos\")    This research site is located on the Seehorn mountain near Davos within a managed subalpine coniferous forest in the Swiss Alps. Seehronwald Davos site is dedicated to forest ecosystem research with current projects focusing on topics of climate change, ecosystem carbon balance, ecophysiology, vegetation and soil sciences. The site belongs to one of the best equipped long-term forest ecology research sites of the world. Time series of climate variables, ecosystem gas exchange (eddy covariance), tree physiology records (sap flow, stem radius changes), and air pollution data cover the history of this site over more than 20 years. Records of local climate variables started in 1876. Since 2013 the site is part of [ICOS](https://www.icos-cp.eu), which awarded the infrastructure the CLASS 1 label on 21 November 2019. The site is part of the following national and international networks and encourages further synergistic collaborations with scientists from all over the world:     * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))  * ICOS Switzerland ([ICOS-CH](https://www.icos-switzerland.ch/davos))  * [TreeNet](https://treenet.info/switzerland/davos): The biological drought and growth indicator network  * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/davos.html))  * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav)  * Ecosystem Research ([ExpeER](http://www.expeeronline.eu/43-expeer-ta-sites/131-davos-seehornwald-switzerland.html))  * Long Term Ecological Research ([LTER](https://www.lter-europe.net))  * [ICP Forests](http://icp-forests.net): the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests    The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the Swiss Federal Institute of Technology Zurich ([ETHZ](https://www.gl.ethz.ch)), and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)) in Birmensdorf and Davos. The infrastructure is provided by the Federal Office of Environment ([FOEN](https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html)). All partners are grateful to forest owners and to the forestry service of the community of Davos for their continuous support.     LWF Plot Davos    - Community: Davos / canton GR  - Date of installation: 15.06.2006  - Size of the plot: 0.6 ha  - Altitude: : 1635-1665  - Geology (in German): Untergrund:  - Oberfl\u00e4che:   - Provisional soil type (WSL):   - Woodland association after EK72: 58: Larici-Piceetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 47.0 cm  - Number of trees BHD >= 12 cm (2006): 498  - Maximum tree age: Picea abies: 200 - 390 yr",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-tea-bag-sites_1.0",
-        "title": "LWF-Tea bag sites",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "7.416653, 46.022611, 9.067072, 47.361944",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816602-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816602-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-tea-bag-sites_1.0",
-        "description": "Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.  The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.  The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-visp-long-term-research-site_1.0",
-        "title": "LWF Visp long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.8583245, 46.2968789, 7.8583245, 46.2968789",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816713-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816713-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-visp-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg \"LWF Visp\")    LWF Plot Visp    - Community: Visp / canton VS  - Date of installation: 13 March 1996  - Size of the plot: 2 ha  - Altitude: 657-733 m  - Mean slope: 80%  - Geology (in German): Penninikum, Jura, B\u00fcndnerschiefer; Kalkphyllite, H\u00e4ngeschutt  - Provisional soil type (WSL): Calcaric Cambisol  - Woodland association after EK72: =~= 38: Arabidi turritae-Quercetum pubescentis  - Main tree species: Pinus sylvestris  - Management system: high forest  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm  - Number of trees BHD >= 12 cm (2011): 650  - Maximum tree age: Pinus sylvestris uneven-aged 40-80 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/visp.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwf-vordemwald-long-term-research-site_1.0",
-        "title": "LWF Vordemwald long-term research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.8867633, 47.2740627, 7.8867633, 47.2740627",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816855-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816855-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/lwf-vordemwald-long-term-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/bffc768d-e3b2-41b2-9b5f-3c1ecbc3ce74/resource/e129ebc8-ef61-4e19-8e89-e18d591ed8c5/download/lwf_vordemwald.jpg \"LWF Vordemwald\")    LWF Plot Vordemwald    - Community: Vordemwald / canton AG  - Date of installation: 18 August 1995  - Size of the plot: 2 ha  - Altitude: 473-487 m  - Mean slope: 14%  - Geology (in German): Untergrund: Oligoz\u00e4n, Aquitanien, untere S\u00fcsswassermolasse, bunte Mergel - Oberfl\u00e4che: Rissgrundmor\u00e4ne  - Soil types (WSL): Distric Gleysols  - Woodland association after EK72: 46: Bazzanio-Abietetum  - Main tree species: Abies alba  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm  - Number of trees BHD >= 12 cm (2011): 1084  - Maximum tree age: Abies alba 110 yr - Quercus sp. 190-210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/vordemwald.html",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-alpthal_1.0",
-        "title": "Alpthal, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.713054, 47.04872, 8.713054, 47.04872",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815452-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815452-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/lwfmeteo-alpthal_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-beatenberg_1.0",
-        "title": "Beatenberg, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.768, 46.7, 7.768, 46.7",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815579-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815579-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/lwfmeteo-beatenberg_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the  Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-bettlachstock_1.0",
-        "title": "Bettlachstock, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.415, 47.223, 7.415, 47.223",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815913-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815913-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/lwfmeteo-bettlachstock_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-celerina_1.0",
-        "title": "Celerina, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.882, 46.499, 9.882, 46.499",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816264-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816264-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/lwfmeteo-celerina_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-chironico_1.0",
-        "title": "Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.816, 46.444, 8.816, 46.444",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816310-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816310-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIiwidW1tIjoiW1wiYmlvbWFzcyBvZiB0b3RhbCBkZWFkIHdvb2RcIixcIkVOVklEQVRcIixcImJpb21hc3Nfb2ZfdG90YWxfZGVhZF93b29kLTcxXCIsXCIxLjBcIiwyNzg5ODE0OTkxLDddIn0%3D/lwfmeteo-chironico_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-isone_1.0",
-        "title": "Isone, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.007, 46.126, 9.007, 46.126",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816327-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816327-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-isone_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-jussy_1.0",
-        "title": "Jussy, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.29, 46.23, 6.29, 46.23",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816343-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816343-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-jussy_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-lausanne_1.0",
-        "title": "Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.653, 46.571, 6.653, 46.571",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816396-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816396-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lausanne_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-lens_1.0",
-        "title": "Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.435198, 46.268368, 7.435198, 46.268368",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816490-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816490-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-lens_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-nationalpark_1.0",
-        "title": "Nationalpark, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "10.236, 46.661, 10.236, 46.661",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816612-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816612-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/lwfmeteo-nationalpark_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-neunkirch_1.0",
-        "title": "Neunkirch, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.53, 47.687, 8.53, 47.687",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816739-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816739-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/lwfmeteo-neunkirch_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-novaggio_1.0",
-        "title": "Novaggio, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.835, 46.023, 8.835, 46.023",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815335-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815335-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/lwfmeteo-novaggio_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-othmarsingen_1.0",
-        "title": "Othmarsingen, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.225, 47.399, 8.225, 47.399",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815361-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815361-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/lwfmeteo-othmarsingen_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-schaenis_1.0",
-        "title": "Sch\u00e4nis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.063, 47.159, 9.063, 47.159",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815414-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815414-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/lwfmeteo-schaenis_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Sch\u00e4nis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Sch\u00e4nis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-visp_1.0",
-        "title": "Visp, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.858, 46.298, 7.858, 46.298",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815495-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815495-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/lwfmeteo-visp_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
-    {
-        "id": "lwfmeteo-vordemwald_1.0",
-        "title": "Vordemwald, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.899, 47.271, 7.899, 47.271",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815732-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815732-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/lwfmeteo-vordemwald_1.0",
-        "description": "High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.",
-        "license": "proprietary"
-    },
     {
         "id": "mac_isl_sat_1",
         "title": "Macquarie Island georeferenced satellite image negative 1:200 000",
@@ -236885,19 +230034,6 @@
         "description": "The Madagascar National Oceanographic Data Centre is attached to the University of Toliara.  Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar\u2019s northeast coast.  This dataset has been collected in Toliara Bay, and includes mollusks, echinoderms, crustaceans, sponges and annelids. It currently consists of 230 records of 7 phylums.",
         "license": "proprietary"
     },
-    {
-        "id": "madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0",
-        "title": "MadCrypto \u2013 Bryophyte and macrolichen diversity in laurel forests of Madeira",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-17.2883606, 32.6278099, -16.6676331, 32.8726671",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815340-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815340-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0",
-        "description": "This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).    The dataset was used for the paper   Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122\u20131133.    The excel file contains 5 sheets:  1) Plot information  2) Bryophyte data with species specific information, separated per substrate  3) Macrolichen data with species specific information, separated per substrate  4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.  5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.",
-        "license": "proprietary"
-    },
     {
         "id": "magnetic_domec_1977_1",
         "title": "Magnetic Readings Along Pioneerskaya - Dome C Traverses, 1977 and 1978",
@@ -236911,19 +230047,6 @@
         "description": "Magnetic readings taken along the Russian traverse from Pioneerskaya to Dome C in 1977 and 1978.  Copies of these documents have been archived in the records store of the Australian Antarctic Division.",
         "license": "proprietary"
     },
-    {
-        "id": "manual-measuring-network_1.0",
-        "title": "Manual measuring network",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "6.842866, 45.933188, 10.42462, 47.272886",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082890-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082890-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/manual-measuring-network_1.0",
-        "description": "The SLF avalanche warning service operates an extensive network of manual measuring sites. The sites are distributed throughout the Swiss Alps and predominantly situated in intermediate altitude zones, between 1000 and 2000 m. Some of the measurement series already span very long periods and are therefore highly valued; the data are also used for climatological and hydrological purposes.    The measuring sites are in fixed locations, which are flat and wind-protected. The observers who perform the measurements are trained and paid by the SLF. Data is collected, as far as possible, from the beginning of November until the end of April and after that until half of the measuring site is snow-free. On some measuring sites event-based measurements are also collected during the summer months. If possible, measurements take place between 7 and 7.30 am local time.      The following variables are measured at all measuring sites:      - snow depth and 24-hour new snow      at numerous sites this additional variable is measured:      - water equivalent of 24-hour new snow (height of the water column in millimeters, if the new snow sample is melted, without changing the base area)          __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__  __To download live data use our [API](https://measurement-api.slf.ch)__.    __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.",
-        "license": "proprietary"
-    },
     {
         "id": "mapss_modis_aerosol_814_1",
         "title": "SAFARI 2000 MAPSS MOD04_L2 Aerosol Summary Data for Southern Africa",
@@ -236950,19 +230073,6 @@
         "description": "The MODIS (Moderate Resolution Imaging Spectroradiometer) Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD05_L2 water vapor product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 24, 2000, through March 4, 2002. The process of generating the statistics involves identifying these locations on the MODIS MOD05_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data product consists of column water-vapor amounts. During the daytime, a near-infrared algorithm is applied over clear land areas of the globe and above clouds over both land and ocean. Over clear ocean areas, water-vapor estimates are provided over the extended glint area. An infrared algorithm for deriving atmospheric profiles is also applied both day and night for Level 2. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following two variables: total column precipitable water vapor (infrared retrieved) and total column precipitable water vapor (near-infrared retrieved).",
         "license": "proprietary"
     },
-    {
-        "id": "marine-fish-occurrences-of-tropical-america_1.0",
-        "title": "marine fish occurrences of Tropical America",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "-124.8046875, -5.9657537, -48.8671875, 37.7185903",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815416-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815416-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/marine-fish-occurrences-of-tropical-america_1.0",
-        "description": "combined and cleaned occurrences of marine fish species of the Greater Caribbean and Tropical East Pacific.    Data were obtain from the following sources in 2019/2020:  https://gbif.org  https://idigbio.org  https://biogeodb.stri.si.edu/sftep/en/pages  https://biogeodb.stri.si.edu/caribbean/en/pages",
-        "license": "proprietary"
-    },
     {
         "id": "marine_mammal_obs_1",
         "title": "Marine Mammal Observations by Greenpeace",
@@ -237028,45 +230138,6 @@
         "description": "MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).",
         "license": "proprietary"
     },
-    {
-        "id": "mass_of_merchantable_branches_of_live_trees-47_1.0",
-        "title": "Mass of merchantable branches of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815616-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815616-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/mass_of_merchantable_branches_of_live_trees-47_1.0",
-        "description": "Dry weight (mass) of branches with a diameter of at least 7 cm from living trees and shrubs  starting at 12cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "mass_of_needles_or_leaves_of_live_trees-49_1.0",
-        "title": "Mass of needles or leaves of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816104-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816104-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIiwidW1tIjoiW1wibHdmIGxhbnRzY2ggbG9uZy10ZXJtIHJlc2VhcmNoIHNpdGVcIixcIkVOVklEQVRcIixcImx3Zi1sYW50c2NoLWxvbmctdGVybS1yZXNlYXJjaC1zaXRlXCIsXCIxLjBcIiwyNzg5ODE1MzMyLDddIn0%3D/mass_of_needles_or_leaves_of_live_trees-49_1.0",
-        "description": "Dry weight (mass) of the needles and leaves of the living trees and shrubs  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "massimo_1.0",
-        "title": "MASSIMO",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815474-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815474-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/massimo_1.0",
-        "description": "MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development.    ![alt text](https://www.envidat.ch/dataset/8fd996d1-aa7e-41b1-ae6d-1192582c62cc/resource/a12e2cfd-da45-4faf-8291-446c5763ac3c/download/massimo2__swissforlab.png)",
-        "license": "proprietary"
-    },
     {
         "id": "maun_met_flux_760_1",
         "title": "SAFARI 2000 Meteorological and Flux Tower Measurements in Maun, Botswana, 2000",
@@ -237249,19 +230320,6 @@
         "description": "Marine and Coastal Management (MCM) is one of four branches of the  Department       of Environmental Affairs and Tourism. It is the regulatory  authority       responsible for managing all marine and coastal activities.  The seal data set       is a collection of seals shot at-sea cruises, and has  been collected from       cruises around the South African Coast, and currently  contains 2440 records of       1 family (Otariidae).",
         "license": "proprietary"
     },
-    {
-        "id": "mean-insect-occupancy-1970-2020_1.0",
-        "title": "Mean insect occupancy 1970\u20132020",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082591-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082591-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mean-insect-occupancy-1970-2020_1.0",
-        "description": "This dataset contains all data, on which the following publication below is based.    **Paper Citation**:    Neff, F., Korner-Nievergelt, F., Rey, E., Albrecht, M., Bollmann, K., Cahenzli, F., Chittaro, Y., Gossner, M. M., Mart\u00ednez-N\u00fa\u00f1ez, C., Meier, E. S., Monnerat, C., Moretti, M., Roth, T., Herzog, F., Knop, E. 2022. Different roles of concurring climate and regional land-use changes in past 40 years' insect trends. Nature Communications, DOI: [10.1038/s41467-022-35223-3](https://doi.org/10.1038/s41467-022-35223-3)    Please cite this paper together with the citation for the datafile. Please also refer to this publication for details on the methods.    ## Summary    Mean annual occupancy estimates for 390 insect species (215 butterflies [Papilionoidea, incl. Zygaenidae moths], 103 grasshoppers [Orthoptera], 72 dragonflies [Odonata]) for nine bioclimatic zones in Switzerland. Covers the years 1970-2020 (for butterflies) and 1980-2020 (for grasshoppers and dragonflies). Mean occupancy denotes the average number of 1 km x 1 km squares in a zone occupied by the focal species. Occupancy estimates stem from occupancy-detection models run with species records data hosted and curated by [info fauna](http://www.infofauna.ch). Data on the level of single MCMC iterations of model fitting are included (4000 sampling iterations). The nine bioclimatic zones were defined based on biogeographic regions and two elevation classes (square above or below 1000 m. asl)",
-        "license": "proprietary"
-    },
     {
         "id": "medical_bibliography_1",
         "title": "A bibliography of polar medicine related articles",
@@ -237275,19 +230333,6 @@
         "description": "This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007.  The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981.  Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually.  The publications are divided into the following areas:  Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters  The fields in this dataset are: Author Title Journal Year",
         "license": "proprietary"
     },
-    {
-        "id": "mega-plots_1.0",
-        "title": "Towards comparable species richness estimates across plot-based inventories - data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-14.0625, 33.1375512, 42.1875, 72.1818036",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/mega-plots_1.0",
-        "description": "The data file refers to the data used in Portier et al. \"Plot size matters: towards comparable species richness estimates across plot-based inventories\" (2022) *Ecology and Evolution*.           This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided.          The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/).",
-        "license": "proprietary"
-    },
     {
         "id": "mendocino_mathison_peak_nff_sr_Not provided",
         "title": "Airborne laser swath mapping (ALSM) data of the San Andreas fault",
@@ -237340,32 +230385,6 @@
         "description": "Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999, and between August 24-September 23, 2000.  The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram.",
         "license": "proprietary"
     },
-    {
-        "id": "meteo-at-s17-antarctica-2018-2019_1.0",
-        "title": "Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "40.0872222, -69.0244444, 40.0872222, -69.0244444",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816331-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816331-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/meteo-at-s17-antarctica-2018-2019_1.0",
-        "description": "This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.    A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.     At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7\u00b0 for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.    The simulations cover a 18 x 18 x 6 m\u00b3 domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.",
-        "license": "proprietary"
-    },
-    {
-        "id": "meteo-stillberg_1.1",
-        "title": "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.86716, 46.773573, 9.86716, 46.773573",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082620-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082620-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/meteo-stillberg_1.1",
-        "description": "# Background information  The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).    #  Station description  The five meteorological stations were all installed at the same location (46\u00b046\u203225.015\u2033N 9\u00b052\u203201.792\u2033E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.    # Data description  We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see \u2018Stillberg_meteo_metadata_stations_v1.csv\u2019). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder \u2018raw_standardized\u2019), edited (folder \u2018raw_edited\u2019) and filtered (folder \u2018filtered\u2019). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.",
-        "license": "proprietary"
-    },
     {
         "id": "meteo_50_1",
         "title": "Meteorology (OTTER)",
@@ -237379,19 +230398,6 @@
         "description": "Meteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data--see also: Canopy Chemistry (OTTER)",
         "license": "proprietary"
     },
-    {
-        "id": "meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0",
-        "title": "Meteorological data used to develop and validate the bias-detecting ensemble (BDE)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816342-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816342-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0",
-        "description": "These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the \"deterministic\" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.",
-        "license": "proprietary"
-    },
     {
         "id": "metnavcpexaw_1",
         "title": "DC-8 Meteorological and Navigation Data CPEX-AW",
@@ -237522,32 +230528,6 @@
         "description": "This data set contains modeled annual net primary production (NPP) for the land biosphere from seventeen different global models. Annual NPP is defined as the net difference of annual carbon uptake (grams CO2/m2/yr) from the atmosphere through photosynthesis by the land vegetation and that lost back to the atmosphere through autotrophic and maintenance respiration. NPP is also related to the Net Ecosystem Exchange (NEE) of carbon accumulated by or lost from the surface by its vegetation and soils. NPP is NEE plus heterotrophic (decomposition) respiration of the vegetation and soils. Only NPP values are included in this data set as some models did not estimate NEE. Data for the mean, standard deviation and coefficient of variation of NPP for the 17 models are provided at spatial resolutions of 1.0 degree and 0.5 degrees. There are two compressed (*.zip) data files with this data set.",
         "license": "proprietary"
     },
-    {
-        "id": "modeling-snow-failure-with-dem_1.0",
-        "title": "Modeling snow failure with DEM",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816442-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816442-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/modeling-snow-failure-with-dem_1.0",
-        "description": "This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.",
-        "license": "proprietary"
-    },
-    {
-        "id": "modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0",
-        "title": "Modeling snow saltation: the effect of grain size and interparticle cohesion",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.1965332, 45.7304689, 8.7121582, 47.1536142",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816540-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816540-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0",
-        "description": "This dataset includes the parallel application and the main results supporting the research article \"Modeling snow saltation: the effect of grain size and interparticle cohesion\" published at the Journal of Geophysical Research: Atmospheres.  The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.",
-        "license": "proprietary"
-    },
     {
         "id": "modis_20day_fast_ice_2",
         "title": "MODIS Composite Based Maps of East Antarctic Fast Ice Coverage",
@@ -237691,19 +230671,6 @@
         "description": "The Moderate Resolution Imaging Spectroradiometer (MODIS) CPEX dataset includes measurements gathered by MODIS during the Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. Data are available from May 9, 2017 through July 16, 2017 in netCDF-3 format.",
         "license": "proprietary"
     },
-    {
-        "id": "mogli-sdm_1.0",
-        "title": "Distribution maps of common woody species for Swiss forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816628-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816628-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/mogli-sdm_1.0",
-        "description": "**We used Swiss National Forest Inventory ([NFI](https://www.lfi.ch/index-en.php)) data to model the potential distribution of the most common woody species for the forested area of Switzerland and provide potential distribution maps that fulfill specific quality criteria with regard to predicting performance.**      More details on the methods and results are described in the project summary available [here](https://www.envidat.ch/dataset/07a9c22c-9ec2-4f49-87e2-3b2d73ad81f2/resource/9bfd5308-d9be-4d01-ab78-48d33889e04e/download/mogli_summary.pdf).    **The resulting maps can be viewed in a simple web-GIS application available at:**    [https://www.lfi.ch/produkte/mogli/mogli-en.php](https://www.lfi.ch/produkte/mogli/mogli-en.php)    **Data can be used without restrictions, but the data must be explicitly asked from the contact person of the dataset in order to obtain access.**  This is a requirement to fulfill the needs of reporting towards the funding agencies.",
-        "license": "proprietary"
-    },
     {
         "id": "mongu_daily_rainfall_785_1",
         "title": "SAFARI 2000 Daily Rainfall Totals for Mongu, Zambia, 1999-2002",
@@ -237808,71 +230775,6 @@
         "description": "Tree basal area, percent tree canopy cover, and proportional contribution of main species to canopy cover were measured at 60 sampling points at 50 m intervals along six transects in the vicinity of the MODIS validation site tower in Kataba Forest, near Mongu, Zambia, in late February to early March 2000 as part of the SAFARI 2000 Wet Season Campaign. The aim of the study was to provide a broad description of the tree canopy layer around the tower.Tree and shrub species composition was recorded for each grid and measurements of canopy cover (% and rank) and frequency of occurrence (%) were made. Basal area was estimated at each grid site in a single 360 degree sweep using a basal area prism. Four estimates of canopy cover, oriented north, south, east, and west around the sample point, were taken at each grid site using a spherical densiometer and the data were averaged to give a single value for each grid. Only the canopies of trees and shrubs above 1.5 m height were measured.The data are stored in an ASCII file, in csv format. The file lists all tree and shrub species recorded and provides the proportional contribution of these species to canopy cover in each grid. Total tree basal area (m2 ha-1) and overall tree canopy cover (%) in each grid is also provided. The companion file provides additional vegetation data, graphics, long-term meteorological data, a discussion of the study results, and photographs of the study site.",
         "license": "proprietary"
     },
-    {
-        "id": "monitoring-of-ash-trees_1.0",
-        "title": "Monitoring of ash trees as part of the Intercantonal Forest Observation Programme",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816750-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816750-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/monitoring-of-ash-trees_1.0",
-        "description": "In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).  We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)",
-        "license": "proprietary"
-    },
-    {
-        "id": "monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0",
-        "title": "Monitoring of Lymantria dispar, Lymantria monacha and Zeiraphera griseana and host food quality in the Swiss Alps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.5778198, 46.3287236, 10.1815796, 46.7443779",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815357-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815357-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0",
-        "description": "The population dynamics of eruptive moths were monitored with pheromone traps and the composition of the larvae's foodplants were analyzed for water content, nitrogen and total phenolics. Moth catches cover a period of 20 years, leaf analyses 10 years. For Zeiraphera griseana (= Z. diniana) only needle analyses are available. The corresponding data on the moth population dynamics are property of A. Fischlin, ETH Z\u00fcrich, and will be made available on EnviDat as well.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mortality-16_1.0",
-        "title": "Mortality",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815431-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815431-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mortality-16_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that died or disappeared between two inventories and that were not harvested. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0",
-        "title": "Mortality of regeneration: Acer spp. and Fagus sylvatica",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "23.5817413, 48.1663717, 23.8097076, 48.2368508",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815541-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815541-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0",
-        "description": "### One individual per species, vitality class (low and high) and height class (eight classes: 0\u201310, 11\u201320, 21\u201335, 36\u201360, 61\u201390, 91\u2013130, 131\u2013200 and 201\u2013500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.     ### Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015\u20132018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.     ## Variables:  * species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica  * species: as above  * dummy: 0 - living individual, 1 - dead individual   * LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]  * tree_age: in years  * avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring\t  * dry_mass_g: aboveground and belowground biomass  * DLI: direct light index (measured only under living individuals)  * BLI: diffuse light index (measured only under living individuals)\t  * GLI: global light index",
-        "license": "proprietary"
-    },
-    {
-        "id": "mortality_star-164_1.0",
-        "title": "Mortality*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815788-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815788-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mortality_star-164_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that died or disappeared between two inventories, but were not cut.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "mosaic-cbers4-brazil-3m-1_NA",
         "title": "CBERS-4/WFI Image Mosaic of Brazil - 3 Months",
@@ -237925,123 +230827,6 @@
         "description": "Landsat-8/OLI image mosaic of Brazil with 30m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the OLI bands 6, 5 and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in July 2017 and ending in June 2018, with a best pixel approach called MEDSTK, which uses the middle of the temporal composition interval to select pixels from the closest dates. More information on MEDSTK can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 8000 Landsat/OLI scenes and was generated based on an existing Landsat Image collection.",
         "license": "proprietary"
     },
-    {
-        "id": "mosaic-s2-amazon-3m-1_NA",
-        "title": "Sentinel-2 image Mosaic of Brazilian Amazon Biome - 3 Months",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2022-06-01",
-        "end_date": "2022-08-31",
-        "bbox": "-74.8710688, -17.1555658, -43.0123868, 5.763264",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204762-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204762-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/mosaic-s2-amazon-3m-1_NA",
-        "description": "Sentinel-2 image mosaic of Brazilian Amazon biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in june 2022 and ending in August 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mosaic-s2-cerrado-2m-1_NA",
-        "title": "Sentinel-2 Image Mosaic of Brazilian Cerrado Biome - 2 Months",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2023-11-01",
-        "end_date": "2024-04-30",
-        "bbox": "-61.4214321, -24.7889766, -40.9255552, -1.7795418",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204178-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204178-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/mosaic-s2-cerrado-2m-1_NA",
-        "description": "Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 02-months of images, starting in November 2023 and ending in April 2024, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mosaic-s2-cerrado-4m-1_NA",
-        "title": "Sentinel-2 image Mosaic of Brazilian Cerrado Biome - 4 Months",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2022-06-01",
-        "end_date": "2022-09-30",
-        "bbox": "-60.4781051, -24.7889766, -40.9255552, -1.7795418",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204669-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204669-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/mosaic-s2-cerrado-4m-1_NA",
-        "description": "Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 04-months of images, starting in june 2022 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mosaic-s2-paraiba-3m-1_NA",
-        "title": "Sentinel-2 image Mosaic of Brazilian Paraiba State - 3 Months",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2019-11-01",
-        "end_date": "2020-01-31",
-        "bbox": "-38.8138995, -8.3980636, -34.7220574, -5.876571",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204427-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204427-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/mosaic-s2-paraiba-3m-1_NA",
-        "description": "Sentinel-2 image mosaic of Brazilian Para\u00edba State with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the Federal University of Para\u00edba's Public Policy Studies Program for Early Childhood Education (NEPPS) by supporting the development of the COVID 19/PB Platform: Relations between Health, Territory and Social Protection in times of sanitary crisis, which created the Platform Covid-19/Para\u00edba: Social and Health Indicator Observatory for SUS and SUAS management. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in November 2019 and ending in January 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 800 Sentinel-2 scenes and was generated based on an existing Sentinel-2 image collection.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mosaic-s2-yanomami_territory-6m-1_NA",
-        "title": "Sentinel-2 image Mosaic of Brazilian Yanomami Indigenous Territory - 6 Months",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2019-04-01",
-        "end_date": "2022-04-01",
-        "bbox": "-66.5059774, -0.4488209, -61.2800511, 4.3426917",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204733-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204733-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/mosaic-s2-yanomami_territory-6m-1_NA",
-        "description": "Sentinel-2 image mosaic of Brazilian Yanomami Indigenous Territory with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the ICIT-FIOCRUZ Health Information Laboratory (LIS) a multi-institutional body coordinated by Fiocruz and the ministry of health, by creating a health situation database of the Yanomami Indigenous Land. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in April 2019 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mosaic-snow-on-sea-ice-data_1.0",
-        "title": "MOSAIC Snow on Sea Ice Data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "96.3982849, 85, 96.3982849, 85",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083009-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083009-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mosaic-snow-on-sea-ice-data_1.0",
-        "description": "Data accompanying David Wagners' Dissertation.   Covers model results and various input from ALPINE3D and SNOWPACK adjusted for sea ice during MOSAiC.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mountain-permafrost-hydrology_1.0",
-        "title": "Mountain Permafrost Hydrology",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816262-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816262-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/mountain-permafrost-hydrology_1.0",
-        "description": "This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.",
-        "license": "proprietary"
-    },
-    {
-        "id": "mountland-jura_1",
-        "title": "Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "6.433333, 46.866667, 6.433333, 46.866667",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816307-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816307-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/mountland-jura_1",
-        "description": "Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.    In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded \u2013 sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.    Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46\u00b054\u2019, E 6\u00b023\u2019) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46\u00b052\u2019, E 6\u00b026\u2019), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d\u2019Aubonne, N 46\u00b051\u2019, E 6\u00b037\u2019), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46\u00b047\u2019, E 6\u00b041\u2019). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.    Soil cores were assembled into rectangular PVC boxes of 60 \uf0b4 80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope S\u00e0rl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.    A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the model\u02bcs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.   The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).    #References    1. Puissant, J., C\u00e9cillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296\u2013305.  2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473\u2013474(0): 465-472.  3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia\u00a0(174)\u00a0: 1425-1435.  4. Peringer A., Siehoff S., Ch\u00e9telat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/  5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/",
-        "license": "proprietary"
-    },
-    {
-        "id": "mr_1.0",
-        "title": "RoRCC",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4535933, 47.3601075, 8.4571981, 47.3616191",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816324-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816324-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/mr_1.0",
-        "description": "The dataset \"RoRCC\" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:  1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020\u20132049, T2: 2045\u20132074, T3: 2070\u20132099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.",
-        "license": "proprietary"
-    },
     {
         "id": "mrmsimpacts_1",
         "title": "Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS V1",
@@ -238120,32 +230905,6 @@
         "description": "Satellite image map of Mt Menzies, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1997.  The map is at a scale of 1:100000, and was produced from Landsat TM and SPOT XS scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, and gives some historical text information. The map has both geographical and UTM co-ordinates.",
         "license": "proprietary"
     },
-    {
-        "id": "multifaceted-diversity-alps_1.0",
-        "title": "Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "4.5922852, 42.8786471, 17.512207, 48.4024632",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082602-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082602-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/multifaceted-diversity-alps_1.0",
-        "description": "This repository contains extensive data for the European Alps:     - Observations of ~3,500 plant species    - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)    - Flora migration rates (categorical) and ecological preferences (continuous indicator values)    - Regional maps of barriers to migration and water bodies at 100-m resolution    - Sampling effort, distance to roads and cities predictors at 100-m resolution    - Present and future abundances over the study region at 1-km resolution (~2,000 species)    - Present and future multifaceted and uniqueness of the European Alps' Flora at  1-km resolution    - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)    - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)    - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them",
-        "license": "proprietary"
-    },
-    {
-        "id": "multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0",
-        "title": "Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.3090973, 46.544946, 8.4725189, 46.6591138",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816349-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816349-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0",
-        "description": "The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.",
-        "license": "proprietary"
-    },
     {
         "id": "musondeimpacts_1",
         "title": "Millersville University Upper Air Radiosondes IMPACTS V1",
@@ -238185,19 +230944,6 @@
         "description": "The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.0 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.",
         "license": "proprietary"
     },
-    {
-        "id": "n-availability-face-hofstetten_1.0",
-        "title": "Nitrogen availability under trees exposed to CO2 enrichment (FACE)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.502, 47.468, 7.5035, 47.469",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815388-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815388-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/n-availability-face-hofstetten_1.0",
-        "description": "Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.",
-        "license": "proprietary"
-    },
     {
         "id": "n_s_dem_248_1",
         "title": "BOREAS DEM Data over the NSA-MSA and SSA-MSA in AEAC Projection",
@@ -238211,19 +230957,6 @@
         "description": "AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.",
         "license": "proprietary"
     },
-    {
-        "id": "nacl_interfacial_phasechanges_1.0",
-        "title": "Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.2213783, 47.5325019, 8.2213783, 47.5325019",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816432-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816432-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/nacl_interfacial_phasechanges_1.0",
-        "description": "Laboratory experiments are presented on the phase change at the surface of sodium chloride \u2013 water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution \u2013 air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.",
-        "license": "proprietary"
-    },
     {
         "id": "nalma_1",
         "title": "North Alabama Lightning Mapping Array (LMA) V1",
@@ -238549,71 +231282,6 @@
         "description": "The NAMMA Lightning ZEUS data is provided by World-ZEUS Long Range Lightning Monitoring Network Data obtained from radio atmospheric signals located at thirteen ground stations spread across the European and African continents and Brazil from August 1, 2006 to October 1, 2006. Lightning activity occurring over a large part of the globe is continuously monitored at varying spatial accuracy (e.g. 10-20 km within and >50 km outside the network periphery) and high temporal (1 msec) resolution. Time is determined by the Arrival Time Difference between the time series from the pairs of the receivers. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.",
         "license": "proprietary"
     },
-    {
-        "id": "nanoplastics-in-forests_1.0",
-        "title": "Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "-23.3789062, 39.6440485, 50.8007813, 68.9131125",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816530-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816530-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/nanoplastics-in-forests_1.0",
-        "description": "The fate of plastic in the environment is of global concern, because its production recently has  increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on  its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known  about the impact of micro and nanoplastics in forest ecosystems.    The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species  through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest  trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and  examined if they were incorporated in different above- and belowground tissues.     Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (\u0394\u03b413C<1\u2030) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.     We acknowledge that the new method developed might be not sensitive enough to unequivocally detect  mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.",
-        "license": "proprietary"
-    },
-    {
-        "id": "napf-ert-monitoring-data_1.0",
-        "title": "Napf ERT monitoring data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.81963, 47.02481, 7.81963, 47.02481",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816636-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816636-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/napf-ert-monitoring-data_1.0",
-        "description": "The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process.    The data is organized in two zip-files:    *   Napf_Raw_BIN.zip: Raw monitoring data in bin-format  *   Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring line    The zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format    The file names contain following abbreviations:    *   Site: Napf   *   Profile: Hor (horizontal profile), Ver (vertical profile)  *   Configuration: WS (Wenner-Schlumberger configuration)  *   Date: Format YYYY-MM-DD  *   Time: Format hhmm",
-        "license": "proprietary"
-    },
-    {
-        "id": "napf-soil-wetness-monitoring-data_1.0",
-        "title": "Napf soil wetness monitoring data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.8153777, 47.0216958, 7.822845, 47.0262011",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082639-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082639-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/napf-soil-wetness-monitoring-data_1.0",
-        "description": "The dataset contains the soil wetness monitoring data from the publication Wicki et al. (2022). It was collected in Wasen i.E. (Napf area, Switzerland).  The monitoring data is quality-controlled and aggregated to hourly values and it is provided for the study period 2019-04-05 to 2022-04-30.    The following information is contained (by column):    * Timestamp (UTC+1 time zone)  * Site: Slope (47.02486 N, 7.81960 E), Flat (47.02302 N, 7.81760 E)  * Sensor type  * Measure: VWC = volumetric water content [m3 m-3], MP = matric potential [hPa], TEMP = temperature [\u00b0C], PREC = precipitation [mm]  * Sensor number (per site each sensor is provided a unique identifier)  * Installation depth [m]  * Homogenization flag: If the data is considered homogeneous, it is given the flag 1, else the flag 0 is given  * Sensor value  * Normalized value: Normalization was conducted for VWC (saturation) and MP values    Wicki, A., Lehmann, P., Hauck, C., and St\u00e4hli, M.: Impact of topography on in-situ soil wetness measurements for regional landslide early warning \u2013 a case study from the Swiss Alpine Foreland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-211, in review, 2022.",
-        "license": "proprietary"
-    },
-    {
-        "id": "nascent-campaign-data-for-motos-et-al-2023_1.0",
-        "title": "NASCENT campaign data for Motos et al. 2023",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082684-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082684-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/nascent-campaign-data-for-motos-et-al-2023_1.0",
-        "description": "The data are described in detail in the paper \"Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds\" (https://doi.org/10.5194/egusphere-2023-530, 2023).  This dataset includes particle number size distribution data from two DMPSs, chemical composition data from a Tof-ACSM, updraft velocity from an ultrasonic anemometer and a wind lidar, cloud droplet number concentration from a HOLIMO and meteorological data (wind speed and direction, temperature).  Note that aerosol composition from a filter pack system, organiccarbon massfrom a high volume sampler and eBC concentration from a MAAP are available on EBAS and therefore not included here",
-        "license": "proprietary"
-    },
-    {
-        "id": "naturalness-of-protective-forests_1.0",
-        "title": "Naturalness of tree species composition in protective forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082740-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082740-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/naturalness-of-protective-forests_1.0",
-        "description": "Data and scripts used by Scherrer et al. 2023 in the publication 'Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition'. The analysis is based on data about the tree species composition of the canopy layer in the NFI4 and information about the potential natural forest of the sites based on the NaiS classification system.",
-        "license": "proprietary"
-    },
     {
         "id": "nauru99_0",
         "title": "Measurements near Nauru, Micronesia in 1999",
@@ -238692,45 +231360,6 @@
         "description": "The NCSU Soundings IMPACTS dataset consists of atmospheric-sounding data collected by the North Carolina State University student sounding club. These data include vertical profiles of atmospheric temperature, relative humidity, pressure, wind speed, and wind direction. These rawinsondes were launched from Raleigh, NC in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The sounding data files are available in netCDF-4 format for February 20, 2020, from February 12, 2023. ",
         "license": "proprietary"
     },
-    {
-        "id": "nead_0.1 (public request for comments)",
-        "title": "Non-Binary Environmental Archive Data (NEAD) format",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.4546355, 47.3605899, 8.4546355, 47.3605899",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815434-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815434-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/nead_0.1%20(public%20request%20for%20comments)",
-        "description": "__Acknowledgement__: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.    The NEAD specifications were presented at the __\"WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century\" (Virtual Conference)__.     -----------------------    __Summary:__  The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.     __License:__ The NEAD specifications are released to the public domain under a Creative Commons CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "neophyte-risk-map-ticino_1.0",
-        "title": "Presence probability risk maps neophyte Ticino",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.4320068, 45.822898, 9.1255188, 46.5605915",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082886-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082886-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/neophyte-risk-map-ticino_1.0",
-        "description": "943 disturbances in the forest of southern Switzerland have been visited and characterized with various general and specific parameters and the presence absence of woody neophyte species has also been recorded.  A Generalized linear regression modelling approach with a binomial family (link function \u201clogit\u201d) was then used to analyse the effects of these parameters on the presence/absence of the six most widespread neophyte species separately (i.e Ailanthus altissima, Buddleja davidii, R. pseudoacacia, Paulownia tomentosa, Prunus laurocerasus, Trachycarpus fortunei). If needed, the models were refitted with the spmodel R-package to account for the spatial dependence. The best model for every species have been used to predict the risk of invasion on a 25 X 25m grid of 1\u2019773\u2019603 million of points covering the entire forest area under 1\u2019500 m a.s.l. Predictions over this new set of points have been computed with the predict function (v4.2.1; R core Team, 2023) and using the best select model for every neophyte species. The resulting prediction are available as a raster tiff.  These presence probability risk maps for the forest area of the entire canton Ticino provide a practical  tool to be used in combination with the waldmonitoring.ch data allowing to efficiently monitor the spread of woody neophyte species in new disturbances in the forest.",
-        "license": "proprietary"
-    },
-    {
-        "id": "net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0",
-        "title": "Net primary productivity (NPP) anomalies simulated by 3-PG model for Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815930-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815930-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0",
-        "description": "Simulated net primary productivity (NPP) anomalies (percent deviation) in 1961-2018 years relative to the 1961\u20131990 reference period for _Picea abies_ and _Fagus sylvatica_. NPP was simulated for the species' potential distribution range in Switzerland on a 1 \u00d7 1 km grid using 3-PG model.  We first assimilated nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017, into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from.   We then estimated the NPP anomalies by first simulating the growth of _P. abies_ and _F. sylvatica_ monocultures with the average climate observed during the 1961\u20131990 period, until the age of 40 years (spin-up). The stands were simulated starting as 2-year-old plantations with an initial density of 10,000 trees/ha. Thinning was performed at age 20 and 35 to reach a final density of ca. 1,000 trees/ha at age 40. We then simulated 30 years forced by monthly resolved climatic data from either the 1961\u20131990 (reference, according to MeteoSwiss) or the 1991\u20132018 period. We neglected the first 40 years of simulations due to high variation in productivity caused by early stage stand development. To study the impact of climate extremes on NPP, we focused on the deviation in NPP (expressed in percentage difference from the reference period) during the 30 year period (age 41\u201370).",
-        "license": "proprietary"
-    },
     {
         "id": "net_carbon_flux_662_1",
         "title": "Net Carbon Dioxide and Water Fluxes of Global Terrestrial Ecosystems, 1969-1998",
@@ -238744,32 +231373,6 @@
         "description": "The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting.",
         "license": "proprietary"
     },
-    {
-        "id": "net_increment-80_1.0",
-        "title": "Net increment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815660-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815660-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/net_increment-80_1.0",
-        "description": "Increment including ingrowth minus the mortality.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "net_increment_star-187_1.0",
-        "title": "Net increment*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815851-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815851-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/net_increment_star-187_1.0",
-        "description": "Increment with ingrowth minus the mortality.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "newcomb_bay_bathy_dem_1",
         "title": "A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands",
@@ -238809,19 +231412,6 @@
         "description": "The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered  during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.",
         "license": "proprietary"
     },
-    {
-        "id": "niche-partitioning-between-wild-bees-and-honeybees_1.0",
-        "title": "Niche partitioning between wild bees and honeybees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4299469, 47.3172277, 8.6949921, 47.4130345",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/niche-partitioning-between-wild-bees-and-honeybees_1.0",
-        "description": "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity.    This dataset contains the raw and processed data supporting the findings from the paper: \"Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city\".     The data contains:    1. Bee trait measurements at the species and individual-level  of five functional traits.    2. The values of the feeding niche partitioning (functional dissimilarity to honeybees)     3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.",
-        "license": "proprietary"
-    },
     {
         "id": "nigeria_marine_Not provided",
         "title": "Nigerian Institute for Oceanography and Marine Research - Marine Species",
@@ -238926,19 +231516,6 @@
         "description": "The NOAA Soundings IMPACTS dataset was collected from January 1, 2020, through March 1, 2023, during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The goal of IMPACTS was to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. The data are available in netCDF-4 format. ",
         "license": "proprietary"
     },
-    {
-        "id": "non-native-native-plant-interactions-in-australian-grasslands_1.0",
-        "title": "Native and no-native plant interactions in Australian grasslands",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "149.3701172, -37.2418448, 150.4083252, -36.2382181",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816323-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816323-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/non-native-native-plant-interactions-in-australian-grasslands_1.0",
-        "description": "This dataset contains all data, on which the following publication below is based.    __Paper Citation:__    _Schlierenzauer, C., Risch, A.C., Sch\u00fctz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._    __Please cite this paper together with the citation for the datafile.__      Study area  The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).   The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).   Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).    Experimental design and sampling  The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36\u00b040\u2019 to 36\u00b042\u2019 S and 149\u00b038\u2019 to 149\u00b042\u2019 E) and three of them are located South (36\u00b051\u2019 to 36\u00b049\u2019 S and 149\u00b038\u2019 to 149\u00b042\u2019 E) of Candelo.   Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.    References  NSW - Department of Planing, Industries and Environment. 2019. \u201cLowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.\u201d https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).  NSW Government - Office of Environment and Heritage. 2017. \u201cLowland Grassy Woodland in the South East Corner Bioregion - Profile.\u201d https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).  Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.  Tozer, Mark et al. 2010. \u201cNative Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.\u201d Cunninghamia\u202f: a journal of plant ecology for eastern Australia 11(3): 359\u2013406.",
-        "license": "proprietary"
-    },
     {
         "id": "npolimpacts_1",
         "title": "NASA S-Band Dual Polarimetric Doppler Radar (NPOL) IMPACTS V1",
@@ -239004,97 +231581,6 @@
         "description": "Near complete coverage of the East Antarctic shield by ice hampers geological       study of crustal architecture important for understanding global tectonic and       climate history. Limited exposures in the central Transantarctic Mountains        (CTAM), however, show that Archean and Proterozoic rocks of the shield as well       as Neoproterozoic-lower Paleozoic sedimentary successions were involved in       oblique convergence associated with Gondwana amalgamation. Subsequently, the       area was overprinted by Jurassic magmatism and Cenozoic uplift.               To extend the known geology of the region to ice-covered areas, we conducted an       aeromagnetic survey flown in draped mode by helicopters over the Transantarctic       Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew       >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of       600 m, with average line spacing 2.5 km over most areas and 1.25 km over       basement rocks exposed in the Miller and Geologists ranges. Additional lines       flown to true north, south and west extended preliminary coverage and tied with       existing surveys. Gravity data was collected on the ground along a central       transect of the helicopter survey area.  From December 2003 to January 2004,       the CTAM group flew a helicopter and twin-otter aeromagnetic survey and       collected gravity station data on the ground in profile form.  These data will       be integrated with other geologic and geophysical data in order to extend the       known geology of the region to ice-covered areas.",
         "license": "proprietary"
     },
-    {
-        "id": "number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0",
-        "title": "Number of natural hazard fatalities per year in Switzerland since 1946",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.6469727, 45.767523, 10.579834, 47.864774",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816460-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816460-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0",
-        "description": "This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.  The following information is contained (by column and column title):      * year  * total number of hazard fatalities  * number of fatalities by flood (German: Hochwasser, \u00dcberschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.  * number of fatalities by debris flow (German: Murgang).  * number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).  * number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).  * number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.  * number of fatalities by lightning (German: Blitz).  * number of fatalities by ice avalanche (German: Eislawine).  * number of fatalities by earthquake (German: Erdbeben).  * number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).    The data was collected based on newspaper research. For more information please refer to     _Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._    The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.    __Restrictions: The data set is not complete.__ Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).    Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).",
-        "license": "proprietary"
-    },
-    {
-        "id": "number_of_forest_edges-124_1.0",
-        "title": "Number of forest edges",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816337-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816337-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number_of_forest_edges-124_1.0",
-        "description": "Number of forest edges according to the NFI definition.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "number_of_forest_plots-125_1.0",
-        "title": "Number of forest plots",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816350-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816350-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number_of_forest_plots-125_1.0",
-        "description": "Number of forest sample plots (Plots).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "number_of_woody_species_from_40_cm_height-144_1.0",
-        "title": "Number of woody species (from 40 cm height)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816548-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816548-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number_of_woody_species_from_40_cm_height-144_1.0",
-        "description": "Number of species of living trees and shrubs  starting at 40 cm plant height that occur within a 200 m2 sample plot.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "number_of_woody_species_gt_12_cm_dbh-41_1.0",
-        "title": "Number of woody species (>= 12 cm DBH)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816627-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816627-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number_of_woody_species_gt_12_cm_dbh-41_1.0",
-        "description": "Number of tree and shrub species  starting at 12 cm dbh (diameter at breast height) within the 200 m2 sample plot.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "number_of_young_forest_plants_by_damage-209_1.0",
-        "title": "Number of young forest plants by damage",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816738-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816738-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/number_of_young_forest_plants_by_damage-209_1.0",
-        "description": "Number of regeneration trees  starting at 10 cm height up to 11.9 cm dbh with a particular type of damage or with no damage. The attribute is recorded by targeting the next regeneration tree in the centre of the subplot during NFI\u2019s regeneration survey. A regeneration tree may have more than one type of damage, which means it may contribute to the total number of regeneration trees for several different types of damage.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "nutrient-addition-stillberg_1.0",
-        "title": "Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.867544, 46.7716544, 9.867544, 46.7716544",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082769-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082769-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/nutrient-addition-stillberg_1.0",
-        "description": "# Background information    The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short\u2010term fertilisation trials persisted in a longer\u2010term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation.   Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha\u22121 a\u22121 was added annually to plots containing 30\u2010year\u2010old *Larix decidua* or 32\u2010year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed.    # Experimental design    Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha\u22121 a\u22121). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* M\u00f6hl et al (2019).    # Data description    The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here:  <https://figshare.com/articles/dataset/Twelve_years_of_low_nutrient_input_stimulates_growth_of_trees_and_dwarf_shrubs_in_the_treeline_ecotone/7025858>",
-        "license": "proprietary"
-    },
     {
         "id": "nwrc_amphibianslowermiss_Not provided",
         "title": "A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley",
@@ -239134,45 +231620,6 @@
         "description": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands.",
         "license": "proprietary"
     },
-    {
-        "id": "observational-data-switzerland-2016-2021_1.0",
-        "title": "Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021 )",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815389-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815389-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/observational-data-switzerland-2016-2021_1.0",
-        "description": "This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_      - danger signs    - human triggered avalanches    - rutschblock test results  (still to be added)     - extended column test results    (still to be added)",
-        "license": "proprietary"
-    },
-    {
-        "id": "observed-and-simulated-snow-profile-data-from-switzerland_1.0",
-        "title": "Observed and simulated snow profile data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082908-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082908-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/observed-and-simulated-snow-profile-data-from-switzerland_1.0",
-        "description": "This data set includes information on all observed and simulated snow profiles that were used to train and validate the  random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.     The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.",
-        "license": "proprietary"
-    },
-    {
-        "id": "observer-driven-pseudoturnover-in-vegetation-surveys_1.0",
-        "title": "Observer-driven pseudoturnover in vegetation surveys",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815537-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815537-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/observer-driven-pseudoturnover-in-vegetation-surveys_1.0",
-        "description": "This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, K\u00fcchler H, K\u00fcchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science.   In the framework of the project \"Monitoring the effectiveness of habitat conservation in Switzerland\", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover.  The excel file contains two sheets:   1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species <0.5 m in height), S (shrub layer; woody species 0.5\u20133 m in height) and T (tree layer; woody species >3 m in height). \"cf.\" indicates uncertain identification, \"aggr.\" indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r \u2259 <0.1%, + \u2259 0.1% to <1%, 1 \u2259 1% to <5%, 2 \u2259 5% to <25%, 3 \u2259 25% to <50%, 4 \u2259 50% to <75%, 5 \u2259 75% to <100%).  2) File used for the linear mixed effects model.",
-        "license": "proprietary"
-    },
     {
         "id": "oldcasey_DSM_2014_1",
         "title": "Digital Surface Model of an area at Old Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 5 February 2014",
@@ -239264,19 +231711,6 @@
         "description": "A new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa. Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations.",
         "license": "proprietary"
     },
-    {
-        "id": "open-science-support-at-wsl_1.0",
-        "title": "Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.4546488, 47.3605728, 8.4546488, 47.3605728",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815825-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815825-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/open-science-support-at-wsl_1.0",
-        "description": "This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.     Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.  EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.    EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.  For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.    Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.    _License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 \"No Rights Reserved\" international license. You can reuse this poster in any way you want, for any purposes and without restrictions.",
-        "license": "proprietary"
-    },
     {
         "id": "orbview_3_Not provided",
         "title": "Orbview-3",
@@ -239290,19 +231724,6 @@
         "description": "OrbView-3 satellite images were collected around the world between 2003 and 2007 by Orbital Imaging Corporation (now GeoEye) at up to one-meter resolution.  The OrbView-3 data set includes 180,000 scenes of one meter resolution panchromatic, black and white, and four meter resolution multi-spectral (color and infrared) data, providing high resolution data useful for a wide range of science applications.  The spacecraft ceased operation on April 23, 2007 and decayed on March 13, 2011 via a controlled reentry into the broad area Pacific Ocean.",
         "license": "proprietary"
     },
-    {
-        "id": "oriental-beech-spectral-and-trait-data_1.0",
-        "title": "Oriental and European beech spectral, traits and genetics data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.35, 48.65, 7.35, 48.65",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082588-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082588-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibnVtYmVyIG9mIGF2YWxhbmNoZSBmYXRhbGl0aWVzIHBlciBjYWxlbmRhciB5ZWFyIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5MzdcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1mYXRhbGl0aWVzLXBlci1jYWxlbmRhci15ZWFyLXNpbmNlLTE5MzZcIixcIjEuMFwiLDI3ODk4MTQ2NDUsN10iLCJ1bW0iOiJbXCJudW1iZXIgb2YgYXZhbGFuY2hlIGZhdGFsaXRpZXMgcGVyIGNhbGVuZGFyIHllYXIgaW4gc3dpdHplcmxhbmQgc2luY2UgMTkzN1wiLFwiRU5WSURBVFwiLFwiYXZhbGFuY2hlLWZhdGFsaXRpZXMtcGVyLWNhbGVuZGFyLXllYXItc2luY2UtMTkzNlwiLFwiMS4wXCIsMjc4OTgxNDY0NSw3XSJ9/oriental-beech-spectral-and-trait-data_1.0",
-        "description": "The dataset includes leaf spectroscopy, leaf traits and genetic data for oriental and european beech trees at two mature forest sites (Allenwiller in France and W\u00e4ldi in Switzerland) sampled in summer 2021 and 2022 for top and bottom of canopy leaves.",
-        "license": "proprietary"
-    },
     {
         "id": "ornl_lai_point_971_1",
         "title": "ISLSCP II Leaf Area Index (LAI) from Field Measurements, 1932-2000",
@@ -239355,19 +231776,6 @@
         "description": "The P-3 Meteorological and Navigation Data IMPACTS dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA\u2019s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data are available in ASCII-ict format from  January 12, 2020, through February 28, 2023.",
         "license": "proprietary"
     },
-    {
-        "id": "p_pet_500m_1.0",
-        "title": "Average precipitation and PET over Switzerland at 500m resolution",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815390-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815390-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/p_pet_500m_1.0",
-        "description": "Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution.  Units are mm per year.  Files are GeoTIFF rasters, and can be read in R using the command raster(\"pcp_ch_longterm_yr_avg.tif), after installing packages \"raster\" and \"rgdal\".",
-        "license": "proprietary"
-    },
     {
         "id": "panpfcov_283_1",
         "title": "BOREAS Prince Albert National Park Forest Cover Data in Vector Format",
@@ -239433,32 +231841,6 @@
         "description": "This project empirically measures the effects of human activity on the behaviour, heart rate and egg-shell surface temperature of Royal penguins on Macquarie Island, as part of ASAC project 1148. This was achieved by collecting behavioural and physiological responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation, guard, creche and moult. Both single person and group approaches were also investigated. Information produced includes minimum approach guidelines.   As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).  Some notes about some of the fields in this dataset:  Temp file refers to whether or not egg shell surface temperature was also recorded for the sample, with the code below refering to the file name.  Neighbour refers to the behavioural control file for each sample (neighbouring nests did not recieve an artificial egg, and provide a behavioural control for responses to human approaches without the scientific treatment).  Nest refers to the randomly used nest markers for each sample.  Heart rate refers to whether heart rate was concurrently recorded with behaviour on the sample (both stored on Hi-8 tape).  Stimulus refers to whether single persons or groups of persons (5 -7, recorded within each sample) were used for the human approaches.  Environment refers to whether approaches were conducted from colony sections abuting pebbly beach or from poa tussock environs (tussock approaches less  than 50 m of the poa / pebbly beach junction).  The code system for nest simply refers to the numbered tag placed at the nest (using three colours, g=green, w=white, b=brown) which were used randomly.  The fields in this dataset are:  Sample Date Breeding Phase Stimulus Type Environment Colony Nest Tape Heart Rate Temp File Neighbour",
         "license": "proprietary"
     },
-    {
-        "id": "pfynwald_2016",
-        "title": "Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "7.61192, 46.30284, 7.61192, 46.30284",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/pfynwald_2016",
-        "description": "To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46\u00b0 18' N, 7\u00b0 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2\u00b0C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.",
-        "license": "proprietary"
-    },
-    {
-        "id": "pfynwaldgasexchange_1.0",
-        "title": "2013-2020 gas exchange at Pfynwald",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/pfynwaldgasexchange_1.0",
-        "description": "Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds.   All metadata can be found in the attached documents.",
-        "license": "proprietary"
-    },
     {
         "id": "phipsimpacts_1",
         "title": "Particle Habit Imaging and Polar Scattering Probe (PHIPS) IMPACTS V1",
@@ -239472,19 +231854,6 @@
         "description": "The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format.",
         "license": "proprietary"
     },
-    {
-        "id": "phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0",
-        "title": "Phosphorus and nitrogen leaching from beech forest soils",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.927478, 50.3518, 10.26725, 52.838967",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0",
-        "description": "Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding  fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.  Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019   with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.   For more details please refer to the publication.",
-        "license": "proprietary"
-    },
     {
         "id": "photo_mosaic_laurens_or_1",
         "title": "Heard Island, Laurens Peninsula, Coastal Orthophoto Mosaic derived from Non-Metric Photography",
@@ -239511,214 +231880,6 @@
         "description": "The Heard Island, Laurens Peninsula, Topographic Data was mapped from Ortho-rectified non-metric photography.  The data consists of Coastline, Crater, Volcano, Island, Lagoon, Water Storage and Watercourse datasets digitised from the photography.",
         "license": "proprietary"
     },
-    {
-        "id": "photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0",
-        "title": "Photogrammetric Drone Data and derived Ground Classification Wolfgang Arelen",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.828043, 46.8307111, 9.8549938, 46.8425715",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082622-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082622-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0",
-        "description": "We conducted two drone flights with the Wingtra and DJI Phantom 4 RTK drones in Davos Wolfgang Arelen, on 25.08.2021. The data was processed with the Agisoft Metashape Professional Software.The Wingtra point cloud was further processed to derive a ground classification in individual LASTools and Terrasolid workflows.",
-        "license": "proprietary"
-    },
-    {
-        "id": "photogrammetric-drone-data-dorfberg_1.0",
-        "title": "Photogrammetric Drone Data Dorfberg",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8224404, 46.8123513, 9.847963, 46.8276943",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082629-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082629-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-dorfberg_1.0",
-        "description": "The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).",
-        "license": "proprietary"
-    },
-    {
-        "id": "photogrammetric-drone-data-gruenboedeli_1.0",
-        "title": "Photogrammetric Drone Data Gr\u00fcenb\u00f6deli",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8807722, 46.8397097, 9.8985161, 46.8545346",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082635-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082635-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-gruenboedeli_1.0",
-        "description": "We conducted various drone flights at Gr\u00fcenb\u00f6deli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.   The following products are available for download:   - DSM 10cm resolution  - Orthomosaic 3cm/25mm resolution  - Snow Raster 10cm resolution  - original RGB images",
-        "license": "proprietary"
-    },
-    {
-        "id": "photogrammetric-drone-data-latschuelfurgga_1.0",
-        "title": "Photogrammetric Drone Data Latschuelfurgga",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.7673884, 46.7935833, 9.793926, 46.8112506",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082666-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082666-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-latschuelfurgga_1.0",
-        "description": "To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latsch\u00fcelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.",
-        "license": "proprietary"
-    },
-    {
-        "id": "photogrammetric-drone-data-schuerlialp_1.0",
-        "title": "Photogrammetric Drone Data Sch\u00fcrlialp",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8921656, 46.7231571, 9.928736, 46.7468439",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082739-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082739-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-schuerlialp_1.0",
-        "description": "The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.",
-        "license": "proprietary"
-    },
-    {
-        "id": "photogrammetric-drone-data-wolfgang-arelen_1.0",
-        "title": "Photogrammetric Drone Data Wolfgang Arelen",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8252503, 46.8303515, 9.8550571, 46.8397178",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082799-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082799-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/photogrammetric-drone-data-wolfgang-arelen_1.0",
-        "description": "We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.",
-        "license": "proprietary"
-    },
-    {
-        "id": "pine-insects-along-elevational-gradients_1.0",
-        "title": "Pine insects along elevational gradients",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.8994141, 45.5935412, 10.1513672, 47.3189659",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815415-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815415-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/pine-insects-along-elevational-gradients_1.0",
-        "description": "The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.    Please contact author for terms of use.",
-        "license": "proprietary"
-    },
-    {
-        "id": "place-attachment-dataset_1.0",
-        "title": "Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.5267639, 47.3535362, 8.5360336, 47.360746",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082958-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082958-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGlzZHJvbWV0ZXIgZGF0YSBnb3RzY2huYWdyYXRcIixcIkVOVklEQVRcIixcImRpc2Ryb21ldGVyLWRhdGEtZ290c2NobmFcIixcIjEuMFwiLDI3ODk4MTQ4ODYsN10iLCJ1bW0iOiJbXCJkaXNkcm9tZXRlciBkYXRhIGdvdHNjaG5hZ3JhdFwiLFwiRU5WSURBVFwiLFwiZGlzZHJvbWV0ZXItZGF0YS1nb3RzY2huYVwiLFwiMS4wXCIsMjc4OTgxNDg4Niw3XSJ9/place-attachment-dataset_1.0",
-        "description": "This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.",
-        "license": "proprietary"
-    },
-    {
-        "id": "plan-statements-for-external-consistency-analysis_1.0",
-        "title": "Plan statements for external consistency analysis: Evidence from Bucharest\u2019s spatial plans",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "25.4311515, 44.1362934, 26.7495109, 44.8260721",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816344-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816344-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/plan-statements-for-external-consistency-analysis_1.0",
-        "description": "The present dataset is part of the published scientific paper Bac\u0103u, S., Gr\u0103dinaru, S. R., & Hersperger, A. M. (2020). Spatial plans as relational data: Using social network analysis to assess consistency among Bucharest\u2019s planning instruments. Land Use Policy, 92.  The goal of this paper was to first develop a theoretical framework for external consistency assessment in spatial plans and then to test the framework with ten spatial plans of the Bucharest (Romania) region. Specifically, the paper has the following workflow: (i) to develop a framework for consistency assessment covering four categories of external consistency; (ii) to extract relevant plan statements from all plans on the four categories; (iii) to assign one-way, symmetrical and contradictory relationships between the extracted plan statements; and (iv) to assess consistencies, inconsistencies and contradictions between plans using directed and valued network analyses. All results were then validated by applying questionnaires to local experts.   The study focuses on a sample consisting of 10 spatial plans of Bucharest that: (1) are currently in force, (2) have spatial implications, (3) involve different administrative levels and (4) come from different planning sectors. The list of the reviewed planning documents can be found in Table 2 of the paper. The framework of consistency assessment contains 24 items, which can be found in Table 1 of the paper. All planning documents were read in respect to all items of the framework in order to extract plan statements used in the analysis. As a result, we provide the plan statement extracted from 10 plans on the 24 items of the framework. All data is in Romanian. The data was discussed qualitatively in the research paper.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-efficacy-computation-based-on-ahp_1.0",
-        "title": "Planning efficacy computation based on AHP",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.5803223, 47.3762671, 8.5803223, 47.3762671",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815581-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815581-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-efficacy-computation-based-on-ahp_1.0",
-        "description": "The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-efficacy-questionnaire-and-interviews_1.0",
-        "title": "Planning efficacy - questionnaire and interviews",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "4.284668, 45.4138765, 12.7441406, 56.170023",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816053-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816053-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-efficacy-questionnaire-and-interviews_1.0",
-        "description": "The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the interviews, the survey, the report explaining survey and a xlxs table to save results.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-intention-in-copenhagen-urban-region_1.0",
-        "title": "Planning intention in Copenhagen urban region",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "11.7498779, 55.4115741, 12.7056885, 56.1529123",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816167-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816167-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-intention-in-copenhagen-urban-region_1.0",
-        "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-intention-in-hannover-urban-region_1.0",
-        "title": "Planning intention in Hannover urban region",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.4688416, 52.1961653, 10.2186584, 52.546635",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816241-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816241-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-intention-in-hannover-urban-region_1.0",
-        "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-intentions-in-strategic-plans-of-european-urban-regions_1.0",
-        "title": "Planning intentions in strategic plans of European urban regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-13.6230469, 34.9647481, 28.9160156, 62.7987017",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816301-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816301-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-intentions-in-strategic-plans-of-european-urban-regions_1.0",
-        "description": "The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions.   The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete \u201cincidents\u201d or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:  \uf0d8\tDate when the information was filled in.  \uf0d8\tName of the urban region and analysed strategic spatial plan .  \uf0d8\tA list of all planning intentions contained in a plan, with each PI being addresses as follows:  \uf02d\tName of PI as it appears in the plan  \uf02d\tTranslated name of the PI (i.e. short name for easy understanding of the meaning)  \uf02d\tExplanation regarding the meaning of the PI  \uf02d\tWhy the PI is considered a priority for the urban region  \uf02d\tSpatial information on the PI (text and cartographic representations).  In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.",
-        "license": "proprietary"
-    },
-    {
-        "id": "planning-intentions-lyon_1.0",
-        "title": "Planning intentions Lyon",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "4.7412872, 45.6763368, 5.0310516, 45.8296515",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816325-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816325-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/planning-intentions-lyon_1.0",
-        "description": "The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.",
-        "license": "proprietary"
-    },
-    {
-        "id": "plant-orthoptera-trophic-networks-lif3web-projet_1.0",
-        "title": "Plant-Orthoptera trophic networks (Lif3web projet)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816397-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816397-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/plant-orthoptera-trophic-networks-lif3web-projet_1.0",
-        "description": "The study of ecological networks along environmental gradients has so far been limited by the difficulty of collecting large-scale dataset of comparable interactions. Here, we compiled 48 plant\u2013orthoptera interaction networks at multiple locations across the Swiss Alps (i.e. along six elevation gradient). Trophic interactions were obtained by applying next-generation sequencing methods (e.g. DNA metabarcoding) on insect feces. Together with interaction data, we also provide data of the functional trait measurement  (i.e. plant leaves traits and insect mandibular strength) expected to influence the realization of the interaction. Species inventories, feces samples and functionals traits were collected during the summer 2016 and 2017. Lab work and network reconstruction were completed in 2019.",
-        "license": "proprietary"
-    },
     {
         "id": "plant_soil_c_n_783_1",
         "title": "SAFARI 2000 Plant and Soil C and N Isotopes, Southern Africa, 1995-2000",
@@ -239758,19 +231919,6 @@
         "description": "AVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated  with estimated canopy carbon and nitrogen content.",
         "license": "proprietary"
     },
-    {
-        "id": "plutonium-239-240-in-southern-italy_1.0",
-        "title": "Plutonium-239+240 and sediment yield data for a small catchment in Southern Italy",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "16.9628906, 39.1672646, 16.9628906, 39.1672646",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082671-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082671-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/plutonium-239-240-in-southern-italy_1.0",
-        "description": "Quantifying the rates of soil redistribution worldwide poses a significant challenge, which has been addressed using various methods such as direct sediment measurements, models, and the use of isotopic, geochemical, and radionuclide tracers. Among these tracers, the isotope of Plutonium, specifically 239+240Pu, is a relatively recent addition to the study of soil redistribution. However, there is still a lack of direct validation for 239+240Pu as a tracer for soil redistribution. To address this gap, we conducted a study in Southern Italy using a unique sediment yield dataset that extends back to the initial fallout of 239+240Pu. Soil samples were collected from the catchment area as well as undisturbed reference sites, and 239+240Pu was extracted, measured using ICP-MS, and converted into soil redistribution rates.",
-        "license": "proprietary"
-    },
     {
         "id": "pmhailclim_1",
         "title": "Passive Microwave Hail Climatology Data Products V1",
@@ -239823,19 +231971,6 @@
         "description": "Optical measurements taken in the Southern Ocean in 2002",
         "license": "proprietary"
     },
-    {
-        "id": "pollination-experiment-insect-traits_1.0",
-        "title": "Pollination experiment: insect traits",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "8.4038544, 47.3003738, 8.6702728, 47.4380272",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816549-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816549-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicGhvc3BoYXRhc2UgbGVhY2hpbmdcIixcIkVOVklEQVRcIixcImpmZXR6ZXItcGhvc3BoYXRhc2UtbGVhY2hpbmdcIixcIjEuMFwiLDI3ODk4MTUzMDMsN10iLCJ1bW0iOiJbXCJwaG9zcGhhdGFzZSBsZWFjaGluZ1wiLFwiRU5WSURBVFwiLFwiamZldHplci1waG9zcGhhdGFzZS1sZWFjaGluZ1wiLFwiMS4wXCIsMjc4OTgxNTMwMyw3XSJ9/pollination-experiment-insect-traits_1.0",
-        "description": "Understanding the interplay of local and landscape-scale drivers of plant-pollinator interactions is crucial to maintaining pollination services in urban environments. The data contains plant-pollinator interactions changed across two independent gradients of local flowering plant species richness and landscape-scale urbanisation level (proportional area of impervious surface within a 500-m radius) in 24 home gardens in the city of Zurich, Switzerland. The data also contains the trait values (tongue length, body size and activity time) of all visiting wild- and honeybees.",
-        "license": "proprietary"
-    },
     {
         "id": "population_counts_BI_1",
         "title": "Adelie penguin population counts for Bechervaise, Verner and Petersen Islands, Mawson",
@@ -239849,19 +231984,6 @@
         "description": "Intermittent Adelie penguin population counts for Bechervaise, Verner and Petersen Islands, Mawson since 1971.  Data include counts of occupied nests for the post 1990/91 data conducted on or about 2nd December. Data collected prior to this were obtained from ANARE Research Notes or field note books. These counts may not have been performed at the 'optimal' time for occupied nests counts, and when this is the case have been adjusted according to a 'correction' factor.  The post 1990/91 data were completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.  The fields in this dataset are:  Year Bechervaise Island Counts Verner Island Counts Petersen Island Counts Date Season occ nests (occupied nests)",
         "license": "proprietary"
     },
-    {
-        "id": "potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0",
-        "title": "Potential driving factors of urban transformations of Austin over 25 years",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-97.7493287, 30.2794116, -97.7493287, 30.2794116",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816648-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816648-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0",
-        "description": "In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., \u2018smart growth\u2019) on urban forms have been poorly measured.  In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.",
-        "license": "proprietary"
-    },
     {
         "id": "potential_veg_xdeg_961_1",
         "title": "ISLSCP II Potential Natural Vegetation Cover",
@@ -239901,71 +232023,6 @@
         "description": "The main goal of this study was to analyze the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes of surface features by the development of relationships between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat ETM+ channels 2, 3, and 4).",
         "license": "proprietary"
     },
-    {
-        "id": "predicted-cloud-droplet-numbers-davos-wolfgang_1.0",
-        "title": "Predicted cloud droplet numbers Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815435-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815435-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/predicted-cloud-droplet-numbers-davos-wolfgang_1.0",
-        "description": "Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the \u201ccharacteristic velocity\u201d approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).",
-        "license": "proprietary"
-    },
-    {
-        "id": "pref-dep-hills_1.0",
-        "title": "Preferential deposition of snow and dust over hills: governing processes and relevant scales",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.0864258, 46.3999881, 7.2619629, 46.6720565",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815520-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815520-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/pref-dep-hills_1.0",
-        "description": "Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the  governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios.    This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed  in the original paper. For additional information, please refer to \"Preferential deposition of snow and dust over hills: governing processes and relevant scales\" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019.",
-        "license": "proprietary"
-    },
-    {
-        "id": "preprocessing-antarctic-weather-station-aws-data-in-python_1.0",
-        "title": "Preprocessing Antarctic Weather Station (AWS) data in python",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, -60",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083020-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083020-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/preprocessing-antarctic-weather-station-aws-data-in-python_1.0",
-        "description": "There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.    The following data sources are taken into account:    * AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)  * ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division  * AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)  * BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)  * CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)  * IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph  * JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)  * NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)  * Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication  * Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d\u2019Urville and Dome C, Amory, 2020)  * PANGAEA: World Data Center (e.g. K\u00f6nig-Langlo, 2012)    __Important notes __    * __Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.__  * Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.  * No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.    Acknowledgements  --------------------------  We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.    References  ---------------  Amory, C. (2020). \u201cDrifting-snow statistics from multiple-year autonomous measurements in Ad\u00e9lie Land, East  Antarctica\u201d. The Cryosphere, 1713\u20131725. doi: 10.5194/tc-14-1713-2020    Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.    K\u00f6nig-Langlo, G. (2012). \u201cContinuous meteorological observations at Neumayer station (2011-01)\u201d. Alfred Wegener  Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.  775173",
-        "license": "proprietary"
-    },
-    {
-        "id": "present-weather-sensor-klosters_1.0",
-        "title": "Present Weather Sensor Klosters",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.880413, 46.869019, 9.880413, 46.869019",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816030-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816030-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/present-weather-sensor-klosters_1.0",
-        "description": "A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of  a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).",
-        "license": "proprietary"
-    },
-    {
-        "id": "production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0",
-        "title": "Production de biogaz \u00e0 partir d\u2019engrais de ferme en Suisse",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816300-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816300-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0",
-        "description": "L'objectif de ce livre blanc est de fournir aux d\u00e9cideurs, aux administrations et aux parties prenantes les r\u00e9sultats de recherche les plus r\u00e9cents afin de promouvoir l'utilisation optimale de la bio\u00e9nergie issue des engrais de ferme dans la transition \u00e9nerg\u00e9tique suisse. A cette fin, les r\u00e9sultats du centre de comp\u00e9tence suisse pour la recherche en bio\u00e9nergie - SCCER BIOSWEET - sont r\u00e9sum\u00e9s et pr\u00e9sent\u00e9s dans un contexte plus large. Si rien d'autre n'est mentionn\u00e9, les r\u00e9sultats se r\u00e9f\u00e8rent \u00e0 la Suisse et, dans le cas de la mati\u00e8re premi\u00e8re, aux potentiels nationaux de biomasse.",
-        "license": "proprietary"
-    },
     {
         "id": "prsondecpexaw_1",
         "title": "Puerto Rico Radiosondes CPEX-AW V1",
@@ -239979,32 +232036,6 @@
         "description": "The Puerto Rico Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 24, 2021 through September 28, 2021 in ASCII format, with associated browse Skew-T graphs in PNG format.",
         "license": "proprietary"
     },
-    {
-        "id": "pv_snow_mountain_1.0",
-        "title": "Dataset on PV Production in Snow Covered Mountains",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816321-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816321-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/pv_snow_mountain_1.0",
-        "description": "### Overview  The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication \"The bright side of PV production in snow-covered mountains\", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.    ### Format  All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.   This is the case for:    - Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),   - Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),   - Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,   - Electricity production from three of our validation sites (/Validation/WSL),   - Measured irradiance for two validation sites (/Validation/ASRB)  The \u2018Metadata\u2019 documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.     ### Structure  The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the __\u2018DataProcessing\u2019__ and the __\u2018functions\u2019__ folder. Functions are called in the different Mains.    __\u2018InputsFromMatlab\u2019__ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.    __\u2018Outputs\u2019__ contains the output files from the different mains (matching names, Main_CHallpixels.m \uf0e0 Prod_CHallpixels)    __\u2018Publication_figures\u2019__ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.    __\u2018Validation\u2019__ contains the data sets used in the model validation:    - Electricity production from three of our validation sites (/WSL),   - Measured irradiance for two validation sites (/ASRB)    __Electricity__ production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.     __Paper Citation:__   > _Annelen Kahl; J\u00e9r\u00f4me Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "r-script-first-stage-sampling_1.0",
-        "title": "R script and input data for \"ALL-EMA sampling design\"",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082897-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082897-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/r-script-first-stage-sampling_1.0",
-        "description": "License: GPL-v2    The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space.    The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward.    Ecker, K.T., Meier, E.S. & Till\u00e9, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.",
-        "license": "proprietary"
-    },
     {
         "id": "r04laifd_293_1",
         "title": "BOREAS RSS-04 1994 Southern Study Area Jack Pine LAI & FPAR Data",
@@ -240083,19 +232114,6 @@
         "description": "CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.",
         "license": "proprietary"
     },
-    {
-        "id": "r3pg-an-r-package-for-simulating-forest-growth_1.0",
-        "title": "r3PG \u2013 An r package for simulating forest growth",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816336-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816336-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/r3pg-an-r-package-for-simulating-forest-growth_1.0",
-        "description": "An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.",
-        "license": "proprietary"
-    },
     {
         "id": "r7laifpa_442_1",
         "title": "BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites",
@@ -240109,58 +232127,6 @@
         "description": "The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images.  Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.",
         "license": "proprietary"
     },
-    {
-        "id": "raclets-backward-trajectories_1.0",
-        "title": "Backward  Trajectories",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816376-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816376-ENVIDAT.html",
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-        "description": "Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.",
-        "license": "proprietary"
-    },
-    {
-        "id": "radar-wind-profiler-davos-wolfgang_1.0",
-        "title": "RADAR Wind profiler Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816529-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816529-ENVIDAT.html",
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-        "description": "The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "radiosondes_1.0",
-        "title": "Radiosondes",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816623-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816623-ENVIDAT.html",
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-        "description": "Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.",
-        "license": "proprietary"
-    },
-    {
-        "id": "ramerenwald-close-range-remote-sensing-benchmark_1.0",
-        "title": "Ramerenwald Close Range Remote Sensing Benchmark",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082632-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082632-ENVIDAT.html",
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-        "description": "Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.  Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.",
-        "license": "proprietary"
-    },
     {
         "id": "ramp Building Footprint Dataset - Accra, Ghana_1",
         "title": "ramp Building Footprint Dataset - Accra, Ghana",
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         "description": "This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,330 tiles and 40,786 buildings. The original dataset was sourced from the [Open Cities AI Challenge Dataset](https://doi.org/10.34911/rdnt.f94cxb) before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412722-MLHUB.html",
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         "description": "This chipped training dataset is over Barishal and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,024 tiles and 41,248 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001597B000). Dataset keywords: Urban, Peri-urban, River",
         "license": "proprietary"
     },
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         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412757-MLHUB.html",
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         "description": "This chipped training dataset is over Bentiu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,789 tiles and 22,396 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (104001004DAECE00). Dataset keywords: Refugee Settlement, Rural. ",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412778-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412778-MLHUB.html",
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         "description": "This chipped training dataset is over Chittagong and parts of the Kutupalong Refugee Camp and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 5,229 tiles and 38,096 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001AC98900). Dataset keywords: Agricultural, Peri-urban, Refugee Camp, Rural",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412678-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412678-MLHUB.html",
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+        "href": "https://cmr.earthdata.nasa.gov/stac/MLHUB/collections?cursor=eyJqc29uIjoiW1wibGFuZGNvdmVybmV0IG5vcnRoIGFtZXJpY2FcIixcIk1MSFVCXCIsXCJMYW5kQ292ZXJOZXQgTm9ydGggQW1lcmljYVwiLFwiMVwiLDI3ODE0MTIyODksMTBdIiwidW1tIjoiW1wibGFuZGNvdmVybmV0IG5vcnRoIGFtZXJpY2FcIixcIk1MSFVCXCIsXCJMYW5kQ292ZXJOZXQgTm9ydGggQW1lcmljYVwiLFwiMVwiLDI3ODE0MTIyODksMTBdIn0%3D/ramp%20Building%20Footprint%20Dataset%20-%20Cox's%20Bazar%2C%20Bangladesh_1",
         "description": "This chipped training dataset is over Cox's Bazaar and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 2,375 tiles and 26,875 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (10400100546E3700). Dataset keywords: Agricultural, Peri-urban, Rural",
         "license": "proprietary"
     },
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412280-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412280-MLHUB.html",
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         "description": "This chipped training dataset is over Dar es Salaam and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 566 tiles and 8,485 buildings. The original dataset was sourced from the [Open Cities AI Challenge Dataset](https://doi.org/10.34911/rdnt.f94cxb) before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.",
         "license": "proprietary"
     },
@@ -240248,7 +232214,7 @@
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         "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412306-MLHUB.umm_json",
         "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2781412306-MLHUB.html",
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         "description": "This chipped training dataset is over Dhaka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp fine-tune Dhaka Model and contains 11,905 tiles and 189,057 individual buildings. The satellite imagery resolution is 30 cm and sourced from Maxar ODP (BG_Dhaka_19Q3_V0_R6C3, ...R6C4, ...R3C2, ...R2C3, ...R3C4). Dataset keywords: Very Dense Urban, Rural, Agricultural, Forested. ",
         "license": "proprietary"
     },
@@ -240261,7 +232227,7 @@
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         "description": "This chipped training dataset is over Shanghai and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,574 tiles and 7,118 buildings. The original dataset was sourced from the [SpaceNet 2 Dataset](https://mlhub.earth/data/spacenet2) before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.",
         "license": "proprietary"
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         "license": "proprietary"
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         "description": "This chipped training dataset is over Wa and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 7,615 tiles and 68,072 individual buildings. The satellite imagery resolution is 32 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-urban",
         "license": "proprietary"
     },
@@ -240486,19 +232452,6 @@
         "description": "The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Tehuantepec, Mexico dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Tehuantepec, Mexico. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.",
         "license": "proprietary"
     },
-    {
-        "id": "raw-data-publication-crossresistance-in-ash-new-phytologist_1.0",
-        "title": "Raw data-Publication cross-resistance in ash - New Phytologist",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
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-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082646-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082646-ENVIDAT.html",
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-        "description": "What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.    Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites.    When and where was the data created / extracted: Summer 2020-2021",
-        "license": "proprietary"
-    },
     {
         "id": "raxpolimpacts_1",
         "title": "Rapid X-band Polarimetric Radar (RaXPol) IMPACTS",
@@ -240512,19 +232465,6 @@
         "description": "The Rapid X-band Polarimetric Radar (RaXPol) IMPACTS dataset consists of data measured from the RaXPol instrument during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The RaXPol dataset consists of various reflectivity variables. RaXPol data are available from January 29, 2022, through January 25, 2023, in netCDF-4 format.",
         "license": "proprietary"
     },
-    {
-        "id": "re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0",
-        "title": "Re-analyzed regional avalanche danger levels in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082703-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082703-ENVIDAT.html",
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-        "description": "The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.     D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.     For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [P\u00e9rez-Guill\u00e9n et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).    For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:  1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.   2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.   3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [P\u00e9rez-Guill\u00e9n et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).     All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.    In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.  - The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.  -  For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.",
-        "license": "proprietary"
-    },
     {
         "id": "readac_d_408_1",
         "title": "BOREAS/AES READAC 15-minute Surface Meteorological Data",
@@ -240577,97 +232517,6 @@
         "description": "The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) Lightning Mapping Array (LMA) was an 11-station, ground-based network located in north-central Argentina from November 2018 to April 2019 in support of the RELAMPAGO field campaign. The RELAMPAGO campaign aimed to characterize the atmospheric conditions and terrain effects that facilitate the initiation and growth of intense weather systems in this region of South America. The LMA maps Very High Frequency (VHF) emissions from lightning in three dimensions. These emissions have also been grouped, temporally and spatially, into individual flashes, and the flash characteristics analyzed to produce gridded products. The dataset was produced by NASA Marshall Space Flight Center (MSFC), via an agreement with the National Oceanic and Atmospheric Administration (NOAA), in order to serve as a validation dataset for the Geostationary Lightning Mapper (GLM). These LMA data are available from November 8, 2018 through April 20, 2019 in ASCII, HDF5, and netCDF-4 format.",
         "license": "proprietary"
     },
-    {
-        "id": "rema-topography-and-antarcticalc2000-for-wrf_1.0",
-        "title": "REMA topography and AntarcticaLC2000 for WRF",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "180, -90, -180, -58",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817063-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817063-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/rema-topography-and-antarcticalc2000-for-wrf_1.0",
-        "description": "Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data  are now available as static data input for the Weather Research and Forecasting model (WRF). Topography  and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods  applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves  the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "reproducibility-dataset-for-cryowrf-validation_1.0",
-        "title": "Reproducibility dataset for CRYOWRF validation",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, -60",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082844-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082844-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/reproducibility-dataset-for-cryowrf-validation_1.0",
-        "description": "This dataset contains data and scripts for \"CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB\" (Gerber et al., submitted).    * Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.  * Static_input: Static topography input file of WRF (geo_em.d01).   * WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.  * WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.  * Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.    Note:    * To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.  * AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.",
-        "license": "proprietary"
-    },
-    {
-        "id": "research-stillberg_1.0",
-        "title": "Bibliography of the long-term treeline research site Stillberg, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.86716, 46.773573, 9.86716, 46.773573",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082894-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226082894-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiYXZhbGFuY2hlIGFjY2lkZW50cyBpbiBzd2l0emVybGFuZCBzaW5jZSAxOTcwLzcxXCIsXCJFTlZJREFUXCIsXCJhdmFsYW5jaGUtYWNjaWRlbnRzLWluLXN3aXR6ZXJsYW5kLXNpbmNlLTE5NzAtNzFcIixcIjEuMFwiLDMyMjYwODEyMTMsMl0iLCJ1bW0iOiJbXCJhdmFsYW5jaGUgYWNjaWRlbnRzIGluIHN3aXR6ZXJsYW5kIHNpbmNlIDE5NzAvNzFcIixcIkVOVklEQVRcIixcImF2YWxhbmNoZS1hY2NpZGVudHMtaW4tc3dpdHplcmxhbmQtc2luY2UtMTk3MC03MVwiLFwiMS4wXCIsMzIyNjA4MTIxMywyXSJ9/research-stillberg_1.0",
-        "description": "# Background information    The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Over almost fifty years, research at the Stillberg site combined long-term monitoring of the large-scale high-elevation afforestation with experimental manipulations simulating global change impacts. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone across different compartments and scales, from individual trees, non-tree vegetation and soils to whole ecosystems, in the context of global change resulting in more than 150 publications.     # Dataset generation    We compiled a comprehensive list of scientific publications covering research at the Stillberg research site by conducting searches in the literature databases Web of Science and Google Scholar, as well as in the Digital Object Repository of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (DORA). We compiled all publications about the afforestation experiment, the FACE \u00d7 warming experiment, the nutrient addition experiment, the G-TREE experiment, as well as other studies related to the Stillberg research site.      # Data description    The Stillberg bibliography (Stillberg_bibliography_data_v1.csv) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals. Currently the bibliography comprises literature about the main afforestation experiment, the FACE \u00d7 warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title. The bibliography is described in a metadata file (Stillberg_bibliography_metadata_v1.csv). The bibliography along with the metadata file are provided in a ZIP-folder (Stillberg_bibliography_v1.zip).",
-        "license": "proprietary"
-    },
-    {
-        "id": "resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0",
-        "title": "Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "4.855957, 42.9451645, 17.5561523, 48.4044086",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815536-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815536-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0",
-        "description": "This dataset comprises a large array of ecological data for the European Alps:        (1) Current soil and climate predictors at various resolutions.      (2) GBIF observations of the European Alps Flora (~4,000 species).      (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)      at various resolutions used in the study to generate (4); except 'expert'...      (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions      (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,      SOIL and CLIM-SOIL models.      (5) Ecological and altitudinal preferences of the European Alps Flora.      (6) Data outputs of the related published article.      (7) All scripts used for analyses.      (8) Additional files used for analyses.      (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations      filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +      non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.",
-        "license": "proprietary"
-    },
-    {
-        "id": "restoring-grassland-multifunctionality_1.0",
-        "title": "Restoring grassland multifunctionality",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.4697723, 47.3929884, 8.7128448, 47.4830893",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815735-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789815735-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/restoring-grassland-multifunctionality_1.0",
-        "description": "Please cite this paper together with the citation for the datafile.    Resch, M. C., Sch\u00fctz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration \u2013 an ecosystem multifunctionality approach. Ecological Applications 31, e02271.            ### Study area    The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altl\u00e4ufe der Glatt (47\u00b027\u2019 to 47\u00b029\u2019 N, 8\u00b037\u2019 to 8\u00b032\u2019 E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 \u00b1 2.0 \u00b0C (January) to 19.0 \u00b1 1.5 \u00b0C (July), and monthly precipitation range from 60 \u00b1 42 mm (January) to 118 \u00b1 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).    ### Experimental design and sampling    A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions.  In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.     Vegetation properties    All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 \u00b0C for 48 h, and weighed (Meyer et al. 2015).     ### Aboveground arthropods    Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was \u201cvacuumed\" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.    ### Belowground fauna     Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouch\u00e9 1977).  To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.    For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 \u00b0C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).     We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014).  Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.    ### Soil chemical and physical properties, soil nitrogen mineralization    We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 \u00b0C until analysis, the other was dried for 48 h at 60 \u00b0C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (\u2264 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).    Ammonium (NH4+) and nitrate (NO3\u2212) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnem\u00fchle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3\u2212 concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 \u00b0C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 \u00b0C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3\u2212 at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3\u2212) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.    To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 \u00b0C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).    Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.    ### References    Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.    Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.    Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.    Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.    Bouch\u00e9, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.    Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschl\u00fcssel der Regenw\u00fcrmer \u00d6sterreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.    Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.    Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensr\u00e4ume der Schweiz: \u00d6kologie \u2010 Gef\u00e4hrdung \u2010 Kennarten, Ott Verlag, Bern.    Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460\u20132461.    Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996\u2013998.    FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.    Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.    Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1\u2014Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.    Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2\u2014Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.    Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.    MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Z\u00fcrich.    Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.    Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Sch\u00fctz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands \u2013 a functional approach. Ecological Applications 0:e02133      Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241\u2013248.    Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Gl\u00f6ckner, L. Tedersoo, I. Saar, U. K\u00f5ljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259\u2013264.    Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.    Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.    Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Gl\u00f6ckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.    Resch, M. C., M. Sch\u00fctz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.    Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimar\u00e3es-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. H\u00e4rdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.    Tedersoo, L., M. Bahram, S. P\u00f5lme, U. K\u00f5ljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. P\u00f5ldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. P\u00e4rtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.    Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2\u2014Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.    Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, \u03b413C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.    Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera \u2013 an outline for soil ecologists. Journal of Nematology 25:315-331.",
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-        "title": "RIT1: Processed permafrost borehole data (2690  m asl), Ritigraben, Switzerland",
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-        "description": "_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._    Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.",
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-        "title": "RIT2: Meteorological station at Ritigraben borehole site",
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-        "description": "Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.",
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         "id": "rivdis_199_1",
         "title": "Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS)",
@@ -240902,32 +232751,6 @@
         "description": "Rocks from Australian Antarctic Division library  This collection turns out to be rather interesting with some of heritage significance.  Box 1 is basically odds and ends but includes a bag of coal from the Prince Charles Mountains worthy of display.  Boxes 2 and 3 probably all are Phil Law collections.  Unfortunately, locality information generally is lacking, but there are some interesting rocks.  Box 1. A.Loose samples Two pale grey, rounded specimens, one with round depression.  Very light weight (low density).  Probably diatomite or radiolarite.  Source?  Dark grey with some red colours.  Fragment of rounded river pebble that has been broken.  Very tough, either quartzite or volcanic rock.  Source?  Scallop (Pecten meridionalis), left valve  Tasmania  Pink and yellow chert, varnished.  One part of outside looks as if it has been fossil wood.  Could be recrystallised chert from fossil wood locality. Source? Could be Tasmanian.  Two small, dark, angular specimens, quite coarse grained with obvious crystal faces that flash.  Specimens are of quartz and galena (PbS).  Source?  Could be west coast Tasmania such as Zeehan.  Three elongate specimens, pale yellow/off white.  They fit together to produce original specimen about 20 cm long.  These are quite common around coastal Australia where rain soaks through sand, dissolves CaCO3 from surface shell material and redeposits it on the way down, perhaps along the roots of a plant.  Goes by various names such as 'fossil roots' (which is wrong), travertine  Large lump of black glass.  Probably furnace slag but could conceivably be volcanic glass (probably too high density for that).  Vesicles (gas bubbles quite common).  B. Sample bag A calico bag of Permian coal from the Prince Charles Mountains.  Bag is labelled to Assistant Director Science but probably was given to Evlyn Barrett as there is a note inside it suggesting that it is a present.  Some specimens are good and could be used for display.  Box 2. A note in the box (from me to Knowles Kerry) notes that these rocks were collected by Phil Law.  While some cards are there, they are not related to the rocks.  Most would appear to be Antarctic.  Sample with cellotape, labelled Cape North.  Fragment of vein quartz.  Pumice.  Grey, very light weight.  Floats.  Product of March 1962 submarine eruption at Protector Shoal in South Sandwich Islands.  Rafts of this pumice circulated around Southern Hemisphere for years, slowly disappearing as the material became dispersed, washed onto beaches (small fragments still common on Australian beaches and some on Heard Island) and as fragments rubbed together, ground small chips off and these sank.  This sample has some flow structure in it from the original eruption and due to elongation of gas bubbles as it flowed and cooled.  It may well be from Heard Island. &It is identical in composition to material collected by Dr Jon Stephenson in 1963 from 'flotsam north of Heard Island' collected during his period on the latter expedition (Stephenson 1964) and identified as having been derived from vast rafts of pumice released in the South Atlantic Ocean during the eruption in the South Sandwich Islands area in 1962 (Gass et al.. 1963).  This is probably the same material referred to by Dr Phil Law, who commented (personal communication, 19 August 1993) that he had seen rafts of pumice near Heard Island in January 1963.& (quote from Quilty and Wheller in preparation for Heard Island symposium of 1998).  Flat dark grey fragment about 1 cm thick.  Otherwise triangular with sharp corners.  Rock is phyllite, rather low grade metamorphic rock, originally a shale in which clay has changed to muscovite to generate the good cleavage. Source?  Would like to know because I have identical material as a glacial erratic from Kerguelen Plateau.  'Granite' Two fragments - angular, one rounded - of grey granite.  Good samples.  They are not quite the same material.  Angular specimen is probably strictly granodiorite (the difference is important only to geologists).  It contains quartz (very pale grey, glassy), two white feldspars (plagioclase-Na-CaAlsSi3O8 - and orthoclase - KalSi3O8) which make up the bulk of the rock in roughly equal proportions and come in two grain sizes - coarse (about 1 cm) and finer (about 2 mm).   Dark minerals are biotite (black mica) and hornblende (complex Fe/Mg silicate). Rounded specimen is more uniform in grain and probably has the same pale minerals but they are not so easy to identify.  Dark mineral hornblende.  Biotite not seen.  There also is a brown mineral, sometimes rhomboid in cross section.  This probably is sphene.  Source of samples?  Rauer Island Rocks.  (Probably Phil Law's own labelling) Replaced in old plastic bag and in turn in a new thin one.  Two glassy (vitreous) grey samples.  Monominerallic.  Vein quartz.  Two flat specimens with marked orientation of very uniform grained constituent minerals.  Both high grade metamorphic rocks - amphibolite gneiss.  Mineralogy - quartz, amphibole (probably hornblende), plagioclase feldspar.  In one the quartz is white and in the other, more yellowish.  Rounded specimen with two rock types in it with clear boundary.  Pale rock is quartzite and other is amphibolite, probably part of same sequence as other amphibolites.  Other rock has great variation in grain size but is otherwise part of the same sequence.  Darker part is amphibolite, coarser than in samples described above and with yellowish quartz and orthoclase.  This rock seems to be the source of the sand grains as it is more friable than others.  Garnet rich sample - Bag 1 One rounded sample contains a significant content of garnet in white 'matrix'.  The pale material is quartz/orthoclase and there is a fine grained, high lustre black mineral that could be magnetite (Fe3 O4).  Source??? Probably a Law sample.  Three specimens in small bag - Bag 2 All are characterised by having quartz veins 1-1.5 cm thick, cutting across the sample and bounded by a layer 1-2 mm thick of a black mineral (amphibole, probably hornblende).  Other constituents of the rock are yellowish quartz, traces of garnet and biotite.  I couldn't identify any feldspar but would expect it.  The rocks, although not labelled with a locality, are very similar to some of those described as from the Rauer Islands but there are some in the Vestfold Hills that are very similar.  Metabasalt? - Bag 4 - two samples These look rather like the basalt dykes that are so characteristic of the Vestfold Hills but are they? And who collected them?  They probably are Phil Law collections.  The dykes were intruded in a series of about 9 episodes from about 2.2 billion to 1.1 billion years.  They have been altered since intrusion and while bulk composition changed little, the mineralogy did.  They are now very tough rocks that break with highly angular, brittle fractures.  Box 3 Judging by the brown sample bag, I suspect these are also Phil Law collections but where from?  Brown calico bag - 5 specimens Large specimen is amphibolite gneiss consisting of layers that are amphibole and biotite rich.  Also has traces of garnet.  Locality?  Two pale specimens.  Both contain prominent garnet in quartz-feldspar matrix, orthoclase dominating.  Metamorphic.  Locality?  Two small specimens.  One is coarser than the other and has obvious garnet with hornblende, biotite, quartz and feldspar.  The other is mainly hornblende/quartz but is a surface specimen, somewhat weathered.  Brown paper bag (now in plastic bag - 5) Small sample (two almost black specimens).  These are different from anything noted above.  While the black biotite is the dominant source of the colour, there is also some quartz and I suspect feldspar.  There also is quite a deal of very fine acicular mineral.   It could be one of several but sillimanite (one of several minerals with the formula Al2SIO3) is a possibility.  Largest, dark sample.  Amphibolite gneiss.  Well banded.  Pale bands of quartz-feldspar-muscovite (white mica).  Dark bands of hornblende-biotite. Source???  Dominantly pale sample with dark patch.  Pale part is quartz-feldspar and the dark is hornblende plus minor acicular mineral (sillimanite?).  Thin sample, 6 x 5 cm, 4 mm thick.  Details not clear.  Too fine grained but probably mainly quartz-feldspar with minor dark mineral (hornblende?).  Plastic bag 6. Large flat specimen and one chip off the large block.  Low grade metamorphic rock, originally fine sandstone. Source?  Plastic bag 7 Rock mainly of coarse K-feldspar and quartz with minor plagioclase.  Rock includes layers of brown mica (phlogopite?).  Metamorphic.  Source?  Plastic bag 8. 8A. 3 specimens (2 are counterparts).  See also 'Brown paper bag' sample above.   Biotite-quartz-sillimanite. 8B. 2 specimens.  Beautiful banded gneiss.  Bands are pale, dominantly quartz and dark, dominantly biotite with some hornblende. 8C. 2 specimens. Quartz-biotite schist with trace of acicular mineral (sillimanite?) and pyrite.  Two remaining specimens. One is of quartz/feldspar(?)/biotite/hornblende-sillimanite?  Is feldspar correctly identified?  Sieve texture.  Other is subrounded boulder, greenish (chlorite?). Patrick G. Quilty AM 22 November 1999",
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-        "id": "rockfall-gallery-testing-parde-2016_1.0",
-        "title": "Rockfall gallery testing Parde 2016",
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-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816316-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/rockfall-gallery-testing-parde-2016_1.0",
-        "description": "Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. \"Durchf\u00fchrung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie\", WSL-Berichte, Heft 68, 2018.",
-        "license": "proprietary"
-    },
-    {
-        "id": "root-traits_1.0",
-        "title": "Root-traits",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.6130533, 46.3023351, 7.6130533, 46.3023351",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816345-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816345-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/root-traits_1.0",
-        "description": "Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald    Data_Fig.1.xlsx  Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring    Data_Tab1+2_2005.xlsx  Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest    Data_Tab1+2_2016.xlsx  Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest",
-        "license": "proprietary"
-    },
     {
         "id": "root_biomass_658_1",
         "title": "Global Distribution of Fine Root Biomass in Terrestrial Ecosystems",
@@ -240941,19 +232764,6 @@
         "description": "A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.",
         "license": "proprietary"
     },
-    {
-        "id": "root_mass_of_live_trees_zell_wutzler-210_1.0",
-        "title": "Root mass of live trees (Zell, Wutzler)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816330-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816330-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/root_mass_of_live_trees_zell_wutzler-210_1.0",
-        "description": "Dry weight (mass) of the belowground part (roots) of living trees and shrubs  starting at 12 cm dbh. The dimensions of the roots are determined according to Zell and Wutzler.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "root_nutrients_659_1",
         "title": "Global Distribution of Root Nutrient Concentrations in Terrestrial Ecosystems",
@@ -241006,19 +232816,6 @@
         "description": "This data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone (\"rooting zone water storage size\") on a 1.0 degree global grid. Two inverse modeling methods were used. The first modeling approach (optimization) was based on the assumption that vegetation has adapted to the environment such that it makes optimum use of water (Kleidon and Heimann 1998). The second method (assimilation) was based on the assumption that green vegetation indicates sufficient available water for transpiration (Knorr 1997). The data set was developed to provide alternative means to describe rooting characteristics of the global vegetation cover for land surface and climate models in support of the ISLSCP Initiative II data collection. There are three files in this data set. ",
         "license": "proprietary"
     },
-    {
-        "id": "ros_data_1.0",
-        "title": "Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816385-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816385-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/ros_data_1.0",
-        "description": "Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of \"virtual meteorological stations\" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in W\u00fcrzer & Jonas et al. (2017), currently under review in Hydrological Processes.",
-        "license": "proprietary"
-    },
     {
         "id": "rs15bmlc_483_1",
         "title": "BOREAS RSS-15 SIR-C and TM Biomass and Landcover Maps of the NSA and SSA",
@@ -241890,110 +233687,6 @@
         "description": "This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W).The global river discharge data set (Coe and Olejniczak 1999), formerly known as the \"Climate, People, and Environment Program (CPEP) Global River Discharge Database,\" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.",
         "license": "proprietary"
     },
-    {
-        "id": "sagehen_cycles_1.0",
-        "title": "Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "-120.3847504, 39.3955455, -120.2007294, 39.464493",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816538-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816538-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/sagehen_cycles_1.0",
-        "description": "Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.    Data include 30-minute time series of     - weather variables,     - sap flow fluxes,     - groundwater levels (in two riparian transects of shallow groundwater wells),     - and stream stages (at 12 sites spanning a 500-meter elevation gradient),    and daily time series of    - temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations    - diel cycle index values for groundwater levels and stream stages,     - and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.    Google Earth Engine scripts for extracting the MODIS data are also provided.",
-        "license": "proprietary"
-    },
-    {
-        "id": "saltation-of-cohesive-granular-materials_1.0",
-        "title": "Saltation of cohesive granular materials",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816677-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816677-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/saltation-of-cohesive-granular-materials_1.0",
-        "description": "The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.     The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.    In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging-27_1.0",
-        "title": "Salvage logging",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816815-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816815-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/salvage_logging-27_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest due to damage occurring (e.g. windthrow, avalanches, insects or rockfall), and not as the result of management planning. This feature is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging_due_to_insects-89_1.0",
-        "title": "Salvage logging due to insects",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816975-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816975-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/salvage_logging_due_to_insects-89_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case insects, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging_due_to_insects_star-251_1.0",
-        "title": "Salvage logging due to insects*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816611-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816611-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/salvage_logging_due_to_insects_star-251_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case insects, and not because of management planning. This feature is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging_due_to_wind-88_1.0",
-        "title": "Salvage logging due to wind",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816712-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816712-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/salvage_logging_due_to_wind-88_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case windthrow, and not due to silvicultural planning. This theme is derived on the level of a sample plot  from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging_due_to_wind_star-250_1.0",
-        "title": "Salvage logging due to wind*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816852-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816852-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/salvage_logging_due_to_wind_star-250_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case windthrow, and not because of management planning. This theme is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "salvage_logging_star-186_1.0",
-        "title": "Salvage logging*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816970-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816970-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/salvage_logging_star-186_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were removed from the forest as a result of damage occurring (e.g. windthrow, avalanches, insects, rockfall), and not because of management planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "samsa94d_462_1",
         "title": "BOREAS/SRC AMS Suite A Surface Meteorological and Radiation Data: 1994",
@@ -242124,19 +233817,6 @@
         "description": "Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.",
         "license": "proprietary"
     },
-    {
-        "id": "satellite-avalanche-mapping-validation_1.0",
-        "title": "Satellite avalanche mapping validation data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.6837616, 46.6742944, 9.9694061, 46.8727491",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817082-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817082-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/satellite-avalanche-mapping-validation_1.0",
-        "description": "Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).    Coordinate system: CH1903+_LV95    The generation of this dataset is described in detail in:    Hafner, E. D., Techel, F., Leinss, S., and B\u00fchler, Y.: Mapping avalanches with satellites \u2013 evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.",
-        "license": "proprietary"
-    },
     {
         "id": "sbuceilimpacts_1",
         "title": "SBU Ceilometers IMPACTS",
@@ -242410,32 +234090,6 @@
         "description": "Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski  (Poland, since 1977), and Ferraz  (Brazil, since 1984).  Admiralty Bay is a protected area within the Antarctic Treaty System, an \u0093Antarctic Specially Managed Area\u0094 (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium.  ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal.  Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research.  The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design.",
         "license": "proprietary"
     },
-    {
-        "id": "schweizerisches-landesforstinventar-2009-2017_1.0",
-        "title": "Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009\u20132017",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/schweizerisches-landesforstinventar-2009-2017_1.0",
-        "description": "Swiss National Forest Inventory. Results of the fourth survey 2009\u20132017.  The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings.  Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland    Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009\u20132017.  In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt,   im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate \u00fcber den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erl\u00e4utert. Der Bericht ist thematisch strukturiert nach den europ\u00e4ischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalit\u00e4t, Holzproduktion, biologische Vielfalt, Schutzwald und Sozio\u00f6konomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab.  Keywords: Waldfl\u00e4che, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversit\u00e4t, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz    Content license: All rights reserved. Copyright \u00a9 2020 by WSL, Birmensdorf.",
-        "license": "proprietary"
-    },
-    {
-        "id": "scolytidae_1.0",
-        "title": "Scolytidae",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817304-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817304-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/scolytidae_1.0",
-        "description": "Scolytidae data from all historic up to the recent projects (29.10.2019) of WSL, collected with various methods in forests of different types.  Data are provided on request to contact person against bilateral agreement.",
-        "license": "proprietary"
-    },
     {
         "id": "scrxsondecpexaw_1",
         "title": "St. Croix Radiosondes CPEX-AW V1",
@@ -242449,19 +234103,6 @@
         "description": "The St. Croix Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment \u2013 Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 19, 2021 through September 14, 2021 in netCDF and ASCII formats, with associated browse imagery in PNG format.",
         "license": "proprietary"
     },
-    {
-        "id": "sdm-env-layers-gdplants_1.0",
-        "title": "Environmental layers for SDM simulations (GDPlants)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817447-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817447-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/sdm-env-layers-gdplants_1.0",
-        "description": "The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).",
-        "license": "proprietary"
-    },
     {
         "id": "sea_elephant_biology_1951_1",
         "title": "Biology of the Sea Elephant (Elephant Seal), Heard Island, 1951",
@@ -242579,32 +234220,6 @@
         "description": "Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara.              PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz",
         "license": "proprietary"
     },
-    {
-        "id": "seasonal-fractional-snow-covered-area-algorithm_1.0",
-        "title": "Seasonal fractional snow-covered area algorithm",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817560-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817560-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seasonal-fractional-snow-covered-area-algorithm_1.0",
-        "description": "This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.    The current version can be found in the WSL/SLF Gitlab repository:   https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area",
-        "license": "proprietary"
-    },
-    {
-        "id": "seasonal-snow-data-wy-2016-2022_1.0",
-        "title": "Seasonal snow data for Switzerland OSHD - FSM2sohd",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083044-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083044-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seasonal-snow-data-wy-2016-2022_1.0",
-        "description": "This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.",
-        "license": "proprietary"
-    },
     {
         "id": "seawater-temp-casey-Dec03_1",
         "title": "Marine water temperatures around Casey station - December 2003",
@@ -242644,32 +234259,6 @@
         "description": "This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern African region selected from global area coverage (GAC) at 4.5-km resolution. The data are provided in HDF format files.",
         "license": "proprietary"
     },
-    {
-        "id": "secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0",
-        "title": "Data for the publication \"Secondary ice production processes in wintertime alpine mixed-phase clouds\"",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.983151, 46.548308, 7.983151, 46.548308",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817759-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817759-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0",
-        "description": "This repository contains all WRF model outputs and observational data sets used for the paper:  Georgakaki, P., Sotiropoulou, G., Vignon, \u00c9., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.",
-        "license": "proprietary"
-    },
-    {
-        "id": "sediment-transport-observations-in-swiss-mountain-streams_1.0",
-        "title": "Sediment transport observations in Swiss mountain streams",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "7.1095008, 46.1234183, 9.5849948, 47.0451026",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817859-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817859-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/sediment-transport-observations-in-swiss-mountain-streams_1.0",
-        "description": "The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan).   Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avan\u00e7on de Nant near Pont de Nant (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport.",
-        "license": "proprietary"
-    },
     {
         "id": "sediments_gom_Not provided",
         "title": "Gulf of Maine Contaminated Sediments Database",
@@ -242683,123 +234272,6 @@
         "description": "The overall objective of this project is to create a database of existing data on contaminants in sediment for the Gulf of Maine region that will be useful to persons throughout the region for scientific and management purposes. This task involves identification of data sources, entry of data into the database format, validation or scientific editing of the database, some analysis and synthesis of the compiled data, and publication of the database and associated bibliographies. The tasks of locating and entering data are being shared among the principle investigators in this project because they require a thorough knowledge of the geographic regions under consideration, an understanding of the types of data identified, and familiarity with active research in these regions. This cooperative approach insures that a more thorough identification and collection of data occurs than could take place from one institution. It also insures that the compiled database will be used by all the participants and their colleagues in the future.    Objectives of the work:  1) Develop a comprehensive inventory (database) of available information on sediment contaminants, both inorganic and organic, for the Gulf of Maine  2) Encourage the cooperation and active participation of multiple agencies and organizations in locating, incorporating, and utilizing the data.  3) Place these and ancillary data in interactive, user-friendly, and readily exchangeable forms (such as desktop computer, FTP, and CD-ROM).  4) Map and analyze sediment contaminant distributions in order to provide the best assemblage of information possible for use in determining contaminant baselines  5) Utilize the database to address specific scientific questions about transport and fate of contaminants in the GOM system.  6) Provide guidance for other agencies and organizations to further the usefulness of the data in research, resource management, and public policy decisions.  7) Provide guidance on where to sample and how to analyze samples in the future to make more effective use of limited resources",
         "license": "proprietary"
     },
-    {
-        "id": "seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0",
-        "title": "Seilaplan Tutorial: DTM download with SwissGeoDownloader",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083074-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083074-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0",
-        "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. The plugin \u2018Swiss Geo Downloader\u2019, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model \u2018swissALTI3D\u2019 with the help of the \u2018Swiss Geo Downloader\u2019 and how to use it for digital planning of a cable line with the plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German!      Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader    Link to Seilaplan website: https://seilaplan.wsl.ch      *********************    F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. Das Plugin Swiss Geo Downloader, welches f\u00fcr das Open Source Geoinformationssystem QGIS zur Verf\u00fcgung steht, erm\u00f6glicht frei verf\u00fcgbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgen\u00f6ssischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue H\u00f6henmodell swissALTI3D herunterladen und f\u00fcr die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.      Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader    Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch",
-        "license": "proprietary"
-    },
-    {
-        "id": "seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0",
-        "title": "Seilaplan Tutorial: Merge DTM tiles",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083081-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083081-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0",
-        "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool \u2018Virtual Raster\u2019. This simplifies the digital planning of a cable line using the QGIS plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German!    Link to Seilaplan website: https://seilaplan.wsl.ch      ***************************    F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenf\u00fcgen und abspeichern kann. F\u00fcr die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgew\u00e4hlt werden.    Link zur Seilaplan-Website: https://seilaplan.wsl.ch",
-        "license": "proprietary"
-    },
-    {
-        "id": "seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0",
-        "title": "Seilaplan Tutorial: DTM download from swisstopo website",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083089-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083089-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0",
-        "description": "In order to use the QGIS plugin \u2018Seilaplan\u2019 for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the \u2018Swiss Geo Downloader\u2019 plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin \u2018Seilaplan\u2019. Please note that the tutorial language is German!    Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details    Link to the rope map website: https://seilaplan.wsl.ch      ********************    F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales H\u00f6henmodell (DHM) n\u00f6tig. Als Alternative zum Swiss Geo Downloader erkl\u00e4ren wir in diesem Tutorial Schritt f\u00fcr Schritt, wie man das n\u00f6tige H\u00f6henmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.     Link zum H\u00f6henmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details    Link zur Seilaplan-Website: https://seilaplan.wsl.ch",
-        "license": "proprietary"
-    },
-    {
-        "id": "seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0",
-        "title": "Seilaplan Tutorial: Load WMS layers as background maps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083098-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083098-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0",
-        "description": "In order to digitally plan a cable line using the QGIS plugin \u2018Seilaplan\u2019, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales,  the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!    Link for the integration of WMS data: https://wms.geo.admin.ch/    Link to the description on the Swisstopo website:  https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html    Link to the Seilaplan website: https://seilaplan.wsl.ch      **************************    F\u00fcr die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Daf\u00fcr m\u00fcssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. F\u00fcr die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massst\u00e4ben, das Vegetationsh\u00f6henmodell LFI oder der Waldmischungsgrad LFI.       Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/    Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html    Link zur Seilaplan-Website: https://seilaplan.wsl.ch",
-        "license": "proprietary"
-    },
-    {
-        "id": "seilaplan_2.0",
-        "title": "Seilaplan",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "4.855957, 43.5878185, 16.1938477, 48.0849294",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817891-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817891-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/seilaplan_2.0",
-        "description": "Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by  Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).  The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.  The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.  The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.   For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).    License: GNU, General Public License, Version 2 or newer.    Literature:  Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.",
-        "license": "proprietary"
-    },
-    {
-        "id": "selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0",
-        "title": "Selected wet snow avalanche activity data Davos, Switzerland (2011-2014)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "9.7084808, 46.6729988, 9.9954987, 46.8658249",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817004-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817004-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0",
-        "description": "Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both.    The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence.    This dataset is the underlying dataset to:  Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.",
-        "license": "proprietary"
-    },
-    {
-        "id": "sensitivity-of-modeled-snow-instability_1.0",
-        "title": "Sensitivity of modeled snow instability",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.8092461, 46.8295484, 9.8092461, 46.8295484",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817141-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817141-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIiwidW1tIjoiW1wic2FsdmFnZSBsb2dnaW5nIGR1ZSB0byBpbnNlY3RzKlwiLFwiRU5WSURBVFwiLFwic2FsdmFnZV9sb2dnaW5nX2R1ZV90b19pbnNlY3RzX3N0YXItMjUxXCIsXCIxLjBcIiwyNzg5ODE2NjExLDddIn0%3D/sensitivity-of-modeled-snow-instability_1.0",
-        "description": "We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario.  The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.",
-        "license": "proprietary"
-    },
-    {
-        "id": "sentinel-1-grd-bundle-1_NA",
-        "title": "Sentinel-1 - Level-1 - Interferometric Wide Swath Ground Range Detected High Resolution",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2021-05-01",
-        "end_date": "2024-06-17",
-        "bbox": "-76.546547, -35.235916, -31.785385, 6.970906",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204188-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204188-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/sentinel-1-grd-bundle-1_NA",
-        "description": "Copernicus Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. This dataset contains interferometric wide swath ground range detected high resolution data available over Brazil.",
-        "license": "proprietary"
-    },
-    {
-        "id": "sentinel-3-olci-l1-bundle-1_NA",
-        "title": "Sentinel-3/OLCI - Level-1B Full Resolution",
-        "catalog": "INPE STAC Catalog",
-        "state_date": "2023-03-04",
-        "end_date": "2024-06-17",
-        "bbox": "-179.431, -45.0723, 179.987, 10.4204",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204728-INPE.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3108204728-INPE.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/INPE/collections?cursor=eyJqc29uIjoiW1wibXlkMTNxMSB2MDA2IC0gY2xvdWQgb3B0aW1pemVkIGdlb3RpZmZcIixcIklOUEVcIixcIm15ZDEzcTEtNi4wXCIsXCJuYVwiLDMxMDgyMDQ0NTMsMV0iLCJ1bW0iOiJbXCJteWQxM3ExIHYwMDYgLSBjbG91ZCBvcHRpbWl6ZWQgZ2VvdGlmZlwiLFwiSU5QRVwiLFwibXlkMTNxMS02LjBcIixcIm5hXCIsMzEwODIwNDQ1MywxXSJ9/sentinel-3-olci-l1-bundle-1_NA",
-        "description": "Copernicus Sentinel-3/OLCI Level-1B product OL_1_EFR (EO processing mode for Full Resolution) over Brazil.",
-        "license": "proprietary"
-    },
     {
         "id": "shadoz_ozonesonde_726_1",
         "title": "SAFARI 2000 SHADOZ Ozonesonde Data, Zambia and Regional Sites, Dry Season 2000",
@@ -242839,45 +234311,6 @@
         "description": "Using the hull mounted Simrad EK500 Scientific Sounder System,             acoustic returns from 38, 120, and 200 kHz transducers were recorded             continuously along ship's track from Aug 3 - Sept 15, 2002.  Of             interest, was the acoustic returns from zooplankton patches and             density structures, and the signel correlations with known plankton             tows and CTD casts.  The survey area included the continental margin             to the west of the Antarctic Peninsula extending from the northern tip             of Adelaide Island to the southern portion of Alexander Island,             Crystal Sound, and Marguerite Bay.  These data have been reduced to             daily files and are supported by software for manipulative purposes.          Ship name/cruise ID/dates of cruise             RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002",
         "license": "proprietary"
     },
-    {
-        "id": "simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0",
-        "title": "Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "9.80934, 46.82962, 9.80934, 46.82962",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817408-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817408-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0",
-        "description": "Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.  Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.",
-        "license": "proprietary"
-    },
-    {
-        "id": "simulated-future-discharge-and-climatological-variables_1.0",
-        "title": "Simulated future discharge and climatological variables for medium-sized catchments in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817564-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817564-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/simulated-future-discharge-and-climatological-variables_1.0",
-        "description": "Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km\u00b2. The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Sch\u00e4fli et al 2007) as objective functions. The calibration and validation procedures are described in detail in K\u00f6plin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, K\u00f6plin et al. 2010).  The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX  for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:    - ICHEC-EC-EARTH\tDMI-HIRHAM5\t2.6\tEUR-11  - ICHEC-EC-EARTH\tDMI-HIRHAM5\t4.5\tEUR-11    - ICHEC-EC-EARTH\tDMI-HIRHAM5\t8.5\tEUR-11    - ICHEC-EC-EARTH\tSMHI-RCA4\t2.6\tEUR-11    - ICHEC-EC-EARTH\tSMHI-RCA4\t4.5\tEUR-11    - ICHEC-EC-EARTH\tSMHI-RCA4\t8.5\tEUR-11    - MOHC-HadGEM2-ES\tSMHI-RCA4\t4.5\tEUR-11    - MOHC-HadGEM2-ES\tSMHI-RCA4\t8.5\tEUR-11    - MPI-M-MPI-ESM-LR\tSMHI-RCA4\t4.5\tEUR-11    - MPI-M-MPI-ESM-LR\tSMHI-RCA4\t8.5\tEUR-11      __*References*__:    -\tK\u00f6plin, N., D. Viviroli, B. Sch\u00e4dler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.  -\tNational Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.  -\tSch\u00e4fli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.  -\tViviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH.  Part II - Physical model description_, Geographica Bernensia, 40, 1-89.  -\tViviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.  -\tViviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation  for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.",
-        "license": "proprietary"
-    },
-    {
-        "id": "simulating-chamois-populations_1.0",
-        "title": "Simulating population divergence of Northern chamois in the Alps based on habitat dynamics",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "4.8, 43.5, 16.3, 48.3",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817711-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817711-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/simulating-chamois-populations_1.0",
-        "description": "# General description    Genomic data, habitat suitability raster files and scripts to run gen3sis to simulate cumulative divergence over time as approximation for genetic differentiation. Scripts for basic analysis of the simulations (e.g., create distance matrix from sampling locations) are provided, too. See original publication (doi link will be provided after publication) for details. The study area are the European Alps.  All data is uploaded as zipped file. Unzip them after the download and put all data in one folder.  See linked publications for correct citation of the data used, use of the data without correct citation is not allowed.    __Corresponding author__: Flurin Leugger, email: flurin.leugger@gmail.com      # Description of the data (content of the different zip folders)    ## Abiotic data    ### Glaciers  Folders with raster stacks with glaciated areas at 0.05\u00b0 resolution in WGS84 projection from Seguinot et al. (2018).  Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., & Preusser, F. (2018). Modelling last glacial cycle ice dynamics in the Alps. _The Cryosphere, 12(10)_, 3265\u20133285. https://doi.org/10.5194/tc-12-3265-2018    ### Rivers  * __river_raster_elevation_class.tif__: raster file (.tif) at 0.05\u00b0 resolution and WGS84 projection with large rivers (scenario 2 from publication). The rivers (each cell) is classified according to the elevation of the cell.  Natural Earth. (2018). Rivers + lake centerlines version 4.1.0. Retrieved January 22, 2020, from https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines  * __river_raster_strahler_class_5km.tif__: raster file at 0.05\u00b0 resolution and WGS84 projection with medium rivers. The rivers are classified according to their Strahler order.  Food and Agriculture Organization of the United Nations. (2014). Rivers in Europe (Derived from HydroSHEDS). Retrieved January 29, 2020, from http://www.fao.org/geonetwork/srv/fr/google.kml?uuid=e0243940-e5d9-487c-8102-45180cf1a99f&layers=AQUAMAPS:37253_rivers_europe    ## Fossil records  * __chamois_fossil_combined_public.xlsx__: list with fossil records until 20,000 years BP from Central Europe, see linked references for citation.     ## Chamois occurrences  * __chamois_occurrence.csv__: Chamois presences from all sources used for the publication (see Suppl. mat. Table S1 for detailed information and correct citations of the data) aggregated at 0.05\u00b0 resolution (~5km).    ## Gen3sis  * __config__: folders with all configuration files used to run the simulations for the publication (different dispersal divergence parameters).  * __scripts__: scripts (and helper functions) to run the gen3sis simulations including scripts for the beginning of the subsequent analysis.    ## Genetic  * __populations.snps.light.vcf__: vcf file of the sampled Northern chamois _(Rupicapra rupicapra)_ . The genomic data encompasses 20k SNPs (from ddRAD sequencing).  * __Sequencing_final_without_slovakia.txt__: sampling locations of Northern chamois _(Rupicapra rupicapra)_    ## HSM  * __habitat_suitability_hindcasting__: Aggregated habitat suitability raster files (stacks, .grd files) at 0.05\u00b0 resolution and WGS84 projection from 20,000 years BP until today in 100 year time steps. There are separate folders for each environmental variable scenario used (different terrain slope variables) an the different occurrence/pseudo-absence sampling strategy used.  * __ODMAP_LeuggerEtAl__2021-10-25.csv__: ODMAP protocol",
-        "license": "proprietary"
-    },
     {
         "id": "sir_c_Not provided",
         "title": "Spaceborne Imaging Radar C-band (SIR-C)",
@@ -242917,19 +234350,6 @@
         "description": "The Sediment Analysis Network for Decision Support (SANDS) Landsat Geological Survey of AL (GSA) Analysis dataset analyzed changes in the coastal shoreline and sedimentation using Landsat GeoTiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project. The daytime GeoTiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area (coordinates 31 to 27 North latitude and 90 to 84.25 West longitude). These are seasonal data for storms from 2001-2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images.",
         "license": "proprietary"
     },
-    {
-        "id": "slow-snow-compression_1.0",
-        "title": "A grain-size driven transition in the deformation mechanism in slow snow compression",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.8417222, 46.8095077, 9.8417222, 46.8095077",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections/slow-snow-compression_1.0",
-        "description": "We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory.     Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep.       The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.",
-        "license": "proprietary"
-    },
     {
         "id": "smart_radiometers_727_1",
         "title": "SAFARI 2000 Surface Atmospheric Radiative Transfer (SMART), Dry Season 2000",
@@ -242982,149 +234402,6 @@
         "description": "Sediment Analysis Network for Decision Support (SANDS) MODIS Gulf Subsetted dataset consists of daytime images for Terra and Aqua MODIS Reflectance bands 8-16, subsetted to 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida) from September 11, 2000 to September 9, 2008. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.",
         "license": "proprietary"
     },
-    {
-        "id": "snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0",
-        "title": "snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "53.4375, 84.2418762, 90, 84.9370543",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817811-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817811-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0",
-        "description": "snowBedFoam 1.0. is a snow transport solver implemented in the computational fluid dynamics software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.   To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian\u2013Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physics-based equations similar to the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018 ; Melo et al., 2022). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.  First, snowBedFoam 1.0. was applied to topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021). Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure (Hames et al., 2022).   Second, snowBedFoam 1.0. was used to simulate the snow distribution around the German Antarctic research station Neumayer Station III. The effect of snow properties, fluid forcing and aerodynamic structures on the snow accumulation were assessed.   snowBedFoam 1.0 was implemented in 2 different OpenFOAM versions, namely OpenFOAM-2.3.0 and OpenFOAM-5.0. The latter offers more options for turbulence models and boundary conditions. The fundamental model equations were not changed from one implementation to the other, thus both still correspond to snowBedFoam 1.0. The two branches are called snowBedFoam-v1-2.3.0 (OpenFOAM-2.3.0)  and snowBedFoam-v1-5.0 (OpenFOAM-5.0). The core codes of snowBedFoam 1.0. are directly accessible on the WSL/SLF GitLab repository (more details in the Resources section).",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-avalanche-data-davos_1.0",
-        "title": "Snow avalanche data Davos, Switzerland, 1999-2019",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.7613525, 46.7398606, 9.9563599, 46.8733358",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817858-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817858-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-avalanche-data-davos_1.0",
-        "description": "These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.    This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the   number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related  to avalanche danger level.    The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).  Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:  Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0",
-        "title": "Snow climate indicators derived from parallel manual snow measurements",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817884-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817884-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0",
-        "description": "Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-deltao18-metamorphism-advection_1.0",
-        "title": "Experiments on stable water isotopes,  snow metamorphism, and advection",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.8473656, 46.8125802, 9.8473656, 46.8125802",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817929-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817929-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/snow-deltao18-metamorphism-advection_1.0",
-        "description": "Stable water isotopes (\u03b418O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the \u03b418O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the \u03b418O content of the snow. The type of metamorphism of the snow had a significant influence on this process.   Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient \u03b418O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0",
-        "title": "Snow depth mapping by airplane photogrammetry (2017 -  ongoing)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.7357177, 46.6471776, 10.072174, 46.8466712",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083092-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083092-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0",
-        "description": "The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.     Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.   The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.   Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.    An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.     The work is published in:",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-depth-mapping_1.0",
-        "title": "Snow Depth Mapping",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.7071075, 46.6838498, 9.9666595, 46.8428318",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817957-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817957-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-depth-mapping_1.0",
-        "description": "The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).  For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the  state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.    The work is published in:     B\u00fchler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-drift-station-3d-ultrasonic_1.0",
-        "title": "Snow Drift Station - 3D Ultrasonic",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.849, 46.859, 9.849, 46.859",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818077-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818077-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-drift-station-3d-ultrasonic_1.0",
-        "description": "A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s&#8254; &#185;]) and air temperature (Ts in [\u00b0C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-drift-station-flowcapt_1.0",
-        "title": "Snow Drift Station - Flowcapt",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.849, 46.859, 9.849, 46.859",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816885-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816885-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-drift-station-flowcapt_1.0",
-        "description": "The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m&#8254; &#178; s&#8254; &#185;) and wind speeds (m s&#8254; &#185;). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-drift-station-micro-rain-radar_1.0",
-        "title": "Snow Drift Station - Micro Rain Radar",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.849, 46.859, 9.849, 46.859",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817056-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817056-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-drift-station-micro-rain-radar_1.0",
-        "description": "The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22\u00b0 with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period.  The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-drift-station-snow-and-air-data_1.0",
-        "title": "Snow Drift Station - Snow and Air Data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.849, 46.859, 9.849, 46.859",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817182-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817182-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-drift-station-snow-and-air-data_1.0",
-        "description": "Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (\u00b0C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (\u00b0C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snow-water-equivalent-for-wagital-catchment-starting-1943_1.0",
-        "title": "Snow water equivalent for reference date April 1 for W\u00e4gital catchment, starting 1943",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2024-01-01",
-        "end_date": "2024-01-01",
-        "bbox": "8.9044619, 47.1065151, 8.9044619, 47.1065151",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817839-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817839-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snow-water-equivalent-for-wagital-catchment-starting-1943_1.0",
-        "description": "Total water reserves of the snow cover [mio m3] for W\u00e4gital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2024, status 2024-04-22.    Funded currently or in the past by:    - Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland  - Meteodat GmbH  - Institute of Geography, University of Zurich  - WSL Institute for Snow and Avalanche Research SLF  - Institute of Geography, ETH Zurich (IAC ETH Zurich)  - AG Kraftwerk W\u00e4gital (AXPO and EWZ)    See also https://www.meteodat.ch/waegital.html",
-        "license": "proprietary"
-    },
     {
         "id": "snow_cover_xdeg_982_1",
         "title": "ISLSCP II Northern Hemisphere Monthly Snow Cover Extent",
@@ -243151,123 +234428,6 @@
         "description": "This data set contains monthly average snow-free surface shortwave albedo calculated for the period 1982-1998 and estimates of background soil/litter reflectances in the visible (0.4-0.7 mm) and near-infrared (NIR) (0.7-1.0 mm) wavelengths. Biophysical Parameters derived from the FASIR-NDVI (Fourier Adjusted, Solar zenith angle correction, Interpolation, and Reconstruction of Normalized Difference Vegetation Index) data set developed for the ISLSCP Initiative II data collection for the months of January 1982 through December 1998 were used to calculate monthly mean surface albedos at 1 X 1 degree spatial resolution for vegetated land surfaces (Sellers et al, 1996b) for the wavelength interval from 0.4 to 3.0 mm. The instantaneous albedo is a function of the properties of the land surface and the solar zenith angle. The monthly mean albedo is an average weighted over time weighted by the incident radiation. NDVI data are used to generate the biophysical parameters leaf area index (LAI) and green fraction of vegetation (Greenness) used by the canopy radiative transfer model of the Simple Biosphere (SiB2) model (Sellers et al, 1996a), which computes the instantaneous albedo. This is coupled to the Colorado State University (CSU) General Circulation Model (GCM) (Randall et al, 1989) which integrates the SiB2 radiative transfer through time. The incident radiation for weighting the time-averaged albedo was provided by a previous run of the GCM using the atmospheric radiation parameterization of Harshvardhan et al (1987). The Harshvardhan parameterization models radiative transfer through the atmosphere in both the longwave and shortwave bands, including the effects of cloudiness and water vapor, carbon dioxide and ozone. The shortwave radiation distinguishes between the direct and diffuse components of the solar beam.",
         "license": "proprietary"
     },
-    {
-        "id": "snowmeltlysimeter-dataset_1.0",
-        "title": "Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.7000024, 47.0288575, 8.7150205, 47.0446816",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817311-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817311-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/snowmeltlysimeter-dataset_1.0",
-        "description": "This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland.  The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.    The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0",
-        "title": "SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "21.62, -72.26, 24.29, -70.46",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817468-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817468-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0",
-        "description": "SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snowmicroquakes_1.0",
-        "title": "Compressive stick slip and snow-micro-quakes",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.8472422, 46.8124617, 9.8472422, 46.8124617",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817590-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817590-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/snowmicroquakes_1.0",
-        "description": "When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature.     The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.",
-        "license": "proprietary"
-    },
-    {
-        "id": "snowmip_1.0",
-        "title": "Weissfluhjoch dataset for ESM-SnowMIP",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.809568, 46.829598, 9.809568, 46.829598",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817738-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817738-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/snowmip_1.0",
-        "description": "This Weissfluhjoch dataset is a processed version  of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.",
-        "license": "proprietary"
-    },
-    {
-        "id": "soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0",
-        "title": "Soil fauna drives SOC storage in a long-term irrigated dry pine forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817877-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817877-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic25vd3BhY2tidW95YW50cGltcGxlZm9hbTogYW4gb3BlbmZvYW0gZXVsZXJpYW4gZXVsZXJpYW4gdHdvLXBoYXNlIHNvbHZlciBmb3IgbW9kZWxsaW5nIGNvbnZlY3Rpb24gb2Ygd2F0ZXIgdmFwb3IgaW4gc25vd3BhY2tzXCIsXCJFTlZJREFUXCIsXCJjb252ZWN0aW9uLWluLXNub3dcIixcIjEuMFwiLDI3ODk4MTQ1ODAsN10iLCJ1bW0iOiJbXCJzbm93cGFja2J1b3lhbnRwaW1wbGVmb2FtOiBhbiBvcGVuZm9hbSBldWxlcmlhbiBldWxlcmlhbiB0d28tcGhhc2Ugc29sdmVyIGZvciBtb2RlbGxpbmcgY29udmVjdGlvbiBvZiB3YXRlciB2YXBvciBpbiBzbm93cGFja3NcIixcIkVOVklEQVRcIixcImNvbnZlY3Rpb24taW4tc25vd1wiLFwiMS4wXCIsMjc4OTgxNDU4MCw3XSJ9/soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0",
-        "description": "Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris).   Data include: (1) properties of soils sampled in 2011 and 2019 (SOC and N concentrations and stocks, soil masses, 13C and 15N natural abundances, C/N ratios, clay content, pH, inorganic C, stoniness, bulk density); (2) litter mass loss and initial litter chemistry of dominant tree species (Quercus, Pinus, Viburnum) from a litter decomposition experiment carried out in 2014-2015; (3) soil fauna abundance sampled in 2015; (4) soil volumetric water content and soil temperature at 10 cm depth measured during the litter decomposition experiment in 2014-2015; (5) soil mesofauna (Acari and Collembola) diversity and community composition from sampling in 2017; (6) irrigation-induced changes in litterfall (2013-2014, 2016-2017), fine-root production (data 2015 from Brunner et al., 2019, Frontiers in Plant Science), annual soil respiration (estimated for 2014-2015), litter mass loss from litter decomposition experiment (May-October 2014), and SOC stocks measured in 2011 and 2019; (7) Moisture dependency of microbial soil respiration (0-10 cm depth, adapted from Joseph et al., 2020 PNAS), soil respiration measured in 2015 and abundance of Acari, Collembola and Lumbricidae sampled in 2015.",
-        "license": "proprietary"
-    },
-    {
-        "id": "soil-moisture-measurements-davos_1.0",
-        "title": "IRKIS Soil moisture measurements Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "9.8297824, 46.7315544, 9.9141504, 46.812365",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817893-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817893-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-moisture-measurements-davos_1.0",
-        "description": "Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.    There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:    * vwc_[stn].smet: containing the soil moisture measurements    * station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.    * interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.    At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.    The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.    Please cite the Wever et al. (2017) reference when using this data in publications.    For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "soil-net-nitrogen-mineralisation-across-global-grasslands_1.0",
-        "title": "Soil net nitrogen mineralisation across global grasslands",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "153.6328125, -40.8470604, -140.2734375, 56.2677611",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817932-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817932-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-net-nitrogen-mineralisation-across-global-grasslands_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Sch\u00fctz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2    Please cite this paper together with the citation for the datafile.    We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/",
-        "license": "proprietary"
-    },
-    {
-        "id": "soil-respiration-exclosure-experiment_1.0",
-        "title": "Soil respiration - exclosure experiment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "10.1273346, 46.6073592, 10.3607941, 46.7580998",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817964-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817964-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic25vd3BhY2tidW95YW50cGltcGxlZm9hbTogYW4gb3BlbmZvYW0gZXVsZXJpYW4gZXVsZXJpYW4gdHdvLXBoYXNlIHNvbHZlciBmb3IgbW9kZWxsaW5nIGNvbnZlY3Rpb24gb2Ygd2F0ZXIgdmFwb3IgaW4gc25vd3BhY2tzXCIsXCJFTlZJREFUXCIsXCJjb252ZWN0aW9uLWluLXNub3dcIixcIjEuMFwiLDI3ODk4MTQ1ODAsN10iLCJ1bW0iOiJbXCJzbm93cGFja2J1b3lhbnRwaW1wbGVmb2FtOiBhbiBvcGVuZm9hbSBldWxlcmlhbiBldWxlcmlhbiB0d28tcGhhc2Ugc29sdmVyIGZvciBtb2RlbGxpbmcgY29udmVjdGlvbiBvZiB3YXRlciB2YXBvciBpbiBzbm93cGFja3NcIixcIkVOVklEQVRcIixcImNvbnZlY3Rpb24taW4tc25vd1wiLFwiMS4wXCIsMjc4OTgxNDU4MCw3XSJ9/soil-respiration-exclosure-experiment_1.0",
-        "description": "Location of data collection  The Swiss National Park (SNP) is located in the southeastern part of Switzerland, and covers an area of 170 km2, 50 km2 of which is forested, 33 km2 is occupied by alpine and 3 km2 by subalpine grasslands. Elevations range from 1350 to 3170 m a.s.l., and mean annual precipitation and temperature are 871 mm and 0.6\u00b0C measured at the Park\u2019s weather station in Buffalora (1980 m a.s.l.) between 1960 and 2009 (MeteoSchweiz 2011). Founded in 1914, the SNP received minimal human disturbance for almost 100 years (no hunting, fishing, or camping, visitors are not allowed to leave the trails). Large (> 1 ha) homogeneous patches of short- and tall-grass vegetation characterize the subalpine grasslands. The average vegetation height of short-grass vegetation is 2 to 5 cm. Red fescue (Festuca rubra L.), quaking grass (Briza media L.) and common bent grass (Agrostis tenuis Sipthrob) are the predominating plant species in this vegetation type. Tussocks of evergreen sedge (Carex sempervirens Vill.) and mat grass (Nardus stricta L.) are predominant in the tall-grass vegetation, which averages 20 cm in vegetation height (Sch\u00fctz and others 2006). Short-grass vegetation developed in areas where cattle and sheep rested (high nutrient input) during agricultural land-use (from 14th century until 1914); tall-grass vegetation developed in areas where cattle and sheep used to graze, but did not rest (Sch\u00fctz and others 2003, 2006). Herbivores were shown to consume > 60% of the biomass in short-grass compared to < 20% in tall-grass vegetation (Sch\u00fctz and others 2006). The herbivore community present in the SNP can be divided into four groups based on body size/weight: large [red deer (Cervus elaphus L.) and chamois (Rupricapra rupricapra L.); 30 - 150 kg], medium [marmot (Marmota marmota L.) and snow hare (Lepus timidus L.); 3 \u2013 6 kg], and small vertebrate herbivores (small rodents: e.g. Clethrionomys spp., Microtus spp., Apodemus spp.; 30 \u2013 100 g) as well as invertebrates (e.g. grasshoppers, caterpillars, cicadas, < 5 g).     Experimental design  We selected 18 subalpine grassland sites (9 short-grass, 9 tall-grass vegetation). The sites were spread across the entire park on dolomite parent material at altitudes of 1975 to 2300 meters. At each site we established an exclosure network (fences) in spring 2009 (early June), immediately after snowmelt. Each exclosure network consisted of a total of five 2 \u00d7 3 m sized plots that progressively excluded the different herbivores listed above (further labeled according to the herbivore guilds that had access to the respective plots \u201cAll\u201d, \u201cMarmot/Mice/Invertebrates\u201d, \u201cMice/Invertebrates\u201d, \u201cInvertebrates\u201d, \u201cNone\u201d). The \u201cAll\u201d treatment was thus accessible to all herbivores, was not fenced and was located at least 5 m away from a 2.1 m tall and 7 \u00d7 9 m main fence that enclosed the other four treatments. This fence was constructed of 10 \u00d7 10 cm wooden posts and electrical equestrian tape (AGRARO ECO, Landi, Bern, Switzerland; 20 mm width) mounted at 0.7 m, 0.95 m, 1.2 m, 1.5 m and 2.1 m above the ground that were connected to a solar charged battery (AGRARO Sunpower S250, Landi, Bern, Switzerland). We also mounted non-electrically charged equestrian tape at 0.5 m to help exclude deer and chamois, yet allow marmots and hares to enter safely. Within each main fenced area we randomly established four 2 \u00d7 3 m plots: (1) The \u201cMarmot/Mice/Invertebrates\u201d plot remained unfenced, thus, with the exception of red deer and chamois, all herbivores were able to access the plot, (2) The \u201cMice/Invertebrates\u201d plot consisted of a 90 cm high electric sheep fence (AGRARO Weidezaunnetz ECO, Landi, Bern, Switzerland; mesh size 10 \u00d7 10 cm) connected to the solar panel and excluded all medium sized mammals (marmots, hares), but provided access for small mammals and invertebrates, (3) The \u201cInvertebrates\u201d plot provided access for invertebrates only and was surrounded by 1 m high metal mesh (Hortima AG, Hausen, Schweiz; mesh size 2 \u00d7 2 cm), (4) The \u201cNone\u201d plot was surrounded by a 1 m tall mosquito net (Sala Ferramenta AG, Biasca, Switzerland; mesh size 1.5 \u00d7 2 mm) to exclude all herbivores. This plot was covered with a roof constructed of a wooden frame lined with mosquito mesh that was mounted on the wooden corner posts. We also treated this plot with a biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) when needed to remove insects that might have entered during data collection or that hatched from the soil.     !!! The here published data set only contains data for \u201cAll\u201d, and \u201cMarmot/Mice/Invertebrates\u201d (= ungulates excluded) plots !!!    Data collection  In-situ soil CO2 emissions were measured with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA) on two randomly selected locations on one subplot within each of the 90 plots. For each measurement the soil chamber (15 cm high; 10 cm diameter) was placed on a permanently installed PVC collar (10 cm diameter) driven five centimeters into the soil at the beginning of the study (June 2009). The measurements were conducted between 0900 and 1700 hours every two weeks from early to early September 2009, 2010, 2011 and 2013. Freshly germinated plants growing within the PVC collars were removed prior to each measurement to avoid measuring plant respiration/photosynthesis. The two measurements collected per plot every two weeks were averaged.     Please acknowledge the funding of the study: funded by the Swiss National Science Foundation, SNF grant-no 31003A_122009/1 to Anita C. Risch, Martin Sch\u00fctz and Flurin Filli",
-        "license": "proprietary"
-    },
-    {
-        "id": "soil-sealing-barcelona-milan_1.0",
-        "title": "Soil sealing Barcelona and Milan different territorial levels",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "1.4941406, 41.1641817, 9.4523621, 45.6562879",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818009-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818009-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-sealing-barcelona-milan_1.0",
-        "description": "__Dataset description__<br />   This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:      * a)\tBarcelona city boundaries   * b)\tBarcelona metropolitan area, \u00c0rea Metropolitana de Barcelona (AMB)   * c)\tBarcelona greater city (Urban Atlas)   * d)\tBarcelona functional urban area (Urban Atlas)   * e)\tMilan city boundaries   * f)\tMilan metropolitan area, Piano Intercomunale Milanese (PIM)   * g)\tMilan greater city (Urban Atlas)   * h)\tMilan functional urban area (Urban Atlas)<br />     In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). <br /> <br />       <br /> __Dataset composition__<br />   The dataset is provided in .csv format and is composed of: <br />     _IMD15_BCN_MI_Sources.csv_: Information on data sources <br />      _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:    * a)\tBarcelona city boundaries (label: bcn_city)    * b)\tBarcelona metropolitan area, \u00c0rea metropolitana de Barcelona (AMB) (label: bcn_amb)   * c)\tBarcelona greater city (Urban Atlas) (label: bcn_grc)   * d)\tBarcelona functional urban area (Urban Atlas) (label: bcn_fua)<br />      _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:    * e)\tMilan city boundaries (label: mi_city)   * f)\tMilan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)   * g)\tMilan greater city (Urban Atlas) (label: mi_grc)   * h)\tMilan functional urban area (Urban Atlas) (label: mi_fua)<br />     _IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). <br />     Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. <br />     In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:     * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);   * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);   * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;    * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.<br /> <br />     <br /> __Further information on the Dataset__<br />   This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.<br />      1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.      2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.     3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.<br />     Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). <br />     Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been \u2018crossed\u2019 (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:     * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The \u2018input\u2019 file is the HRL IMD raster file as described in point 1) and the \u2018output\u2019 file is each of the spatial/territorial files. The option \"Use Input Features for Clipping Geometry (optional)\u201d was selected for each of the clipping.    * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the \"overwrite\" option has been selected. <br />    <br /> Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.<br />     The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.",
-        "license": "proprietary"
-    },
     {
         "id": "soil125r_309_1",
         "title": "Data over the SSA in Raster Format and AEAC Projection",
@@ -243333,19 +234493,6 @@
         "description": "Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.",
         "license": "proprietary"
     },
-    {
-        "id": "solar-biomass-additional-references_1.0",
-        "title": "Linking solar and biomass resources to generate renewable energy: can we find local complementarities in the agricultural setting?",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083029-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083029-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/solar-biomass-additional-references_1.0",
-        "description": "Additional references to the article:  Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting? Gillianne Bowman, Thierry Huber, Vanessa Burg Energies, https://www.mdpi.com/1996-1073/16/3/1486   Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems.",
-        "license": "proprietary"
-    },
     {
         "id": "soller_wetlands_674_1",
         "title": "LBA Regional Freshwater Wetlands, 1-Degree (Stillwell-Soller et al.)",
@@ -243385,32 +234532,6 @@
         "description": "Mysticete whale calls were monitored/recorded via deployment of             directional sonobuoys during March-August 2001.  This monitoring             technique is used to study whale distribution, behavior and aid in             estimating populations.  Deployments were either random or when whales             were observed.  The observed calls are identified by species.             Ancillary calls by seals are reported but not identified by species.             The survey area included the continental margin to the west of the             Antarctic Peninsula extending from the northern tip of Adelaide Island             to the southern portion of Alexander Island, Crystal Sound, and             Marguerite Bay.          Ship names/cruise ID/cruise dates                R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001                RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001                RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001          Access to the original acoustic recordings should be directed to the             Investigator identified in this description.",
         "license": "proprietary"
     },
-    {
-        "id": "source-code-climate-change-scenarios-at-hourly-resolution_1.0",
-        "title": "Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816944-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816944-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-code-climate-change-scenarios-at-hourly-resolution_1.0",
-        "description": "This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling    This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper.    This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.",
-        "license": "proprietary"
-    },
-    {
-        "id": "sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0",
-        "title": "Sources and turnover of soil organic matter in Pfynwald irrigation experiment",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083043-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083043-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0",
-        "description": "This dataset contains all data on which the following publication below is based.    Paper Citation:  Guidi, C., Lehmann, M.M., Meusburger, K., Saurer, M., Vitali, V., Peter, M., Brunner, I., Hagedorn, F. (accepted). Tracing sources and turnover of soil organic matter in a long-term irrigated dry forest using a novel hydrogen isotope approach. Soil Biology and Biochemistry.    Please cite this paper together with the citation for the datafile.    Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris).  Data include: (1) Isotopic composition (stable isotope ratios of non-exchangeable hydrogen \u03b42Hn, carbon \u03b413C, and nitrogen \u03b415N) and Hn, C and N concentrations in SOM sources (fresh Pinus sylvestris needles, litter layer, fine roots), bulk SOM (organic layer, 0-2 cm, 2-5 cm, 60-80 cm), particle-size fractions (depths:  0-2 cm, 2-5 cm; cPOM: coarse POM; fPOM: fine POM; MOM: mineral-associated organic matter); (2) Mass loss, \u03b42Hn values and Hn concentrations of Pinus sylvestris fine roots and needle litter (litter decomposition experiments from Herzog et al. 2019, ISME journal, and Guidi et al. 2022, Global Change Biology); (3) Relative source contribution (foliar litter, fine roots, and mycelia) to bulk SOM and fractions estimated using Bayesian mixing models (R package MixSIAR, version 3.1.12) with irrigation and depth as fixed factors. The models were informed with \u03b413C, \u03b415N and \u03b42Hn values and C, N, and Hn concentrations of foliar litter, roots, and mycelia as input sources. Given the kinetic isotope fractionation occurring during microbial SOM decomposition, the mixing models were informed with isotope fractionation factors, representing the isotope enrichment from sources to soils; (4) Fraction of new organic Hn (Fnew) over the irrigation period, calculated using a simple end-member mixing model according to Balesdent et al. (1987) and mean residence time estimated as MRT = - t / ln (1 - Fnew), with t time in years since irrigation started and assuming single-pool model with first-order kinetics.",
-        "license": "proprietary"
-    },
     {
         "id": "sowers_0739491_Not provided",
         "title": "2008 South Pole Firn Air Methane Isotopes",
@@ -243424,45 +234545,6 @@
         "description": "This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica.  The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br).  The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity.  The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air.  The project will involve two phases:  1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories.  Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport.",
         "license": "proprietary"
     },
-    {
-        "id": "spatial-modelling-of-ecological-indicator-values_1.0",
-        "title": "Spatial modelling of ecological indicator values",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817163-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817163-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-modelling-of-ecological-indicator-values_1.0",
-        "description": "Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.     This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection (\"+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\"). The excel file (xlsx) provides a short description of the raster layers.    Paper Citation:    Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)",
-        "license": "proprietary"
-    },
-    {
-        "id": "spatial-planning-brazil_1.0",
-        "title": "Spatially explicit data to evaluate spatial planning outcomes in a coastal region in S\u00e3o Paulo State, Brazil",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "-46.1425781, -24.005155, -44.4836426, -23.1908626",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817270-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817270-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic25vd3BhY2tidW95YW50cGltcGxlZm9hbTogYW4gb3BlbmZvYW0gZXVsZXJpYW4gZXVsZXJpYW4gdHdvLXBoYXNlIHNvbHZlciBmb3IgbW9kZWxsaW5nIGNvbnZlY3Rpb24gb2Ygd2F0ZXIgdmFwb3IgaW4gc25vd3BhY2tzXCIsXCJFTlZJREFUXCIsXCJjb252ZWN0aW9uLWluLXNub3dcIixcIjEuMFwiLDI3ODk4MTQ1ODAsN10iLCJ1bW0iOiJbXCJzbm93cGFja2J1b3lhbnRwaW1wbGVmb2FtOiBhbiBvcGVuZm9hbSBldWxlcmlhbiBldWxlcmlhbiB0d28tcGhhc2Ugc29sdmVyIGZvciBtb2RlbGxpbmcgY29udmVjdGlvbiBvZiB3YXRlciB2YXBvciBpbiBzbm93cGFja3NcIixcIkVOVklEQVRcIixcImNvbnZlY3Rpb24taW4tc25vd1wiLFwiMS4wXCIsMjc4OTgxNDU4MCw3XSJ9/spatial-planning-brazil_1.0",
-        "description": "The present dataset is part of the published scientific paper entitled \u201cThe role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil\u201d (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in S\u00e3o Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.    We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the S\u00e3o Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.    1)\tLand use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase    We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.     Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).    To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).     The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.    2)\tFederal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:    The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).    3)\tTopographic drivers    To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).",
-        "license": "proprietary"
-    },
-    {
-        "id": "species-distribution-maps-gdplants_1.0",
-        "title": "Species distribution maps of Fagales and Pinales (GDPlants)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817446-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817446-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-distribution-maps-gdplants_1.0",
-        "description": "This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).   To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).",
-        "license": "proprietary"
-    },
     {
         "id": "spectra_licor_47_1",
         "title": "Leaf Reflectances: LICOR (OTTER)",
@@ -243554,45 +234636,6 @@
         "description": "Spectral reflectance measurements of flat field targets as reference points representative of pseudo-invariant targets as measured by Spectron SE590 spectrophotometer",
         "license": "proprietary"
     },
-    {
-        "id": "spherical-model-snow-compression-3dct_1.0",
-        "title": "Unconfined compression experiments and 3D CT images of spherical model snow and RG snow samples",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.8472691, 46.8125931, 9.8472691, 46.8125931",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817624-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817624-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/spherical-model-snow-compression-3dct_1.0",
-        "description": "For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles.   In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).",
-        "license": "proprietary"
-    },
-    {
-        "id": "spot6-avalanche-outlines-16-january-2019_1.0",
-        "title": "SPOT6 Avalanche outlines 16 January 2019",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "7.8744507, 46.5266506, 10.5152893, 47.3248149",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817742-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817742-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-avalanche-outlines-16-january-2019_1.0",
-        "description": "Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)    The generation of the data is described in:    B\u00fchler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.    The data was comprehensivly validated in a subset area in     Hafner, E.D.; Techel, F.; Leinss, S.; B\u00fchler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021",
-        "license": "proprietary"
-    },
-    {
-        "id": "spot6-avalanche-outlines-24-january-2018_1.0",
-        "title": "SPOT6 Avalanche outlines 24 January 2018",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.6522217, 45.8900082, 10.4754639, 47.15984",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817830-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817830-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-avalanche-outlines-24-january-2018_1.0",
-        "description": "Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)    The generation of the data is described in:    B\u00fchler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.    Abstract.   Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12\u2019500 km2).     We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18\u2019737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.     This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.    Koordinate System: CH1903+ LV95 LN02",
-        "license": "proprietary"
-    },
     {
         "id": "spot_3s_437_1",
         "title": "BOREAS Level-3S SPOT Imagery: Scaled At-Sensor Radiance in LGSOWG Format",
@@ -243697,71 +234740,6 @@
         "description": "This data set was prepared by the SERM-FBIU.  The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.",
         "license": "proprietary"
     },
-    {
-        "id": "stability-tests-avalanche-observations-switzerland-norway_1.0",
-        "title": "Stability tests, avalanche observations, Switzerland, Norway",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "4.0429688, 44.0286335, 33.2226563, 71.0474315",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817868-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817868-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-tests-avalanche-observations-switzerland-norway_1.0",
-        "description": "Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.  The data set consists of     - Rutschblock test results (Switzerland)    - Extended Column Test results (Switzerland, Norway)    - Avalanche occurrence data (Switzerland, Norway).    The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).  For further information regarding the data, please refer to the publication or contact the author.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0",
-        "title": "Stable Water Isotopes in snow and vapor on the Weissfluhjoch",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.8094821, 46.8298249, 9.8094821, 46.8298249",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817892-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817892-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0",
-        "description": "Notice:  Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.    This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).   Snow profiles and surface samples are available at different frequencies for each campaign. Please see \"Data_description.pdf\" for details.     Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0",
-        "title": "Stand inventory data from the 10\u2010ha forest research plot in Uholka, Ukraine",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "23.6268711, 48.2747904, 23.6268711, 48.2747904",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817954-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817954-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0",
-        "description": "In 2000, a permanent forest plot of 10 ha has been established in the core zone of the primeval beech forest of Uholka.   All living and dead trees with a diameter at breast height (DBH) \u2265 60 mm were identified to species, DBH measured, stems tagged and mapped.   Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories.   In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka\u2010Shyrokyi Luh forest. This data set allows us to derive a high\u2010resolution digital elevation model (DEM) of the plot area.  The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stand_density_sdi-29_1.0",
-        "title": "Stand density (SDI)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817931-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817931-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stand_density_sdi-29_1.0",
-        "description": "The Stand Density Index (SDI) is a general measure for the density of a stocking and is based on the number of stems/ha and the average diameter of the tally trees on the sample plot.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0",
-        "title": "Stated preference data on the insurance value of forests in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817992-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817992-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0",
-        "description": "We present stated preference data for improved forest management measures from seven Swiss municipalities in the Cantons of Grisons and Valais. The data was collected between October 2019 and February 2020 using an online questionnaire. We invited 10289 households to participate and received 939 responses. The online questionnaire consisted of two main parts: (i) an online choice experiment and (ii) questions on the sociodemographic characteristics of the responding households. The choice experiment confronted households with twelve consecutive choice tasks. Each choice task consisted of three options with a varying degree of avalanche and rock fall risk reduction due to improved forest management. The options further differed with respect to the way the costs for the improved forest management are allocated and the way they are calculated. We additionally provided each of the options with a cost attribute, allowing for the calculation of willingness to pay measures. At the end of the choice experiment we asked five de-briefing questions and eight attitudinal questions. Additionally, we asked the responding households to state their willingness to take risks. The sociodemographic characteristics collected in the second part of the questionnaire allow for an analysis of the impact they have on the choices we observed in the first part of the questionnaire.",
-        "license": "proprietary"
-    },
     {
         "id": "stations_drainage_modelling_1",
         "title": "Drainage modelling for Australia's year-round Stations in Antarctica and at Macquarie Island",
@@ -243775,97 +234753,6 @@
         "description": "This GIS dataset is the result of modelling of surface water drainage for Australia's year-round stations in Antarctica (Casey, Davis, Mawson) and at Macquarie Island. This was done by the Australian Antarctic Data Centre in 2000 at the request of Dr Martin Riddle and Dr Ian Snape of the Australian Antarctic Division.  The modelling was done using ESRI's ArcInfo workstation. A digital elevation model (DEM) was first created from the the Australian Antarctic Data Centre's topographic data, principally surface contours, and then drainage basins and drainage paths were derived from the DEM. The drainage is predicted surface flow due to changes in elevation and doesn't take account of any other processes. Several DEMs were created for each station at different spatial extents and resolutions.  The origin of the topographic data was mapping from aerial photography. The aerial photography was flown on 4 January 1994 (Casey), 11, 12 February 1997 (Davis), 7 December 1994 (Macquarie Island) and 18 March 1996 (Mawson).  The data available for download includes for each station:  1 the DEMs and the topographic data from which they were created; and 2 the predicted drainage basins and drainage paths. The data was originally created in ESRI's coverage (vector) and grid (raster) formats. It is provided here in ESRI's file geodatabase format. Documentation is included with the data.  The modelling was done as an aid to fuel spill contingency planning and the predicted drainage paths were used in the production of a spill risk assessment map for each station to go with the Australian Antarctic Division's fuel spill contingency plan for each station. The maps are available from the SCAR Map Catalogue (see a Related URL) and have catalogue numbers 13702 to 13705.  Validation of the modelling for Casey is described in M.J.Riddle, I.Snape, D.T.Smith and A.Z.Woinarski, 'Development and validation of a GIS-based dispersion model for oil spills in snow covered ground' in Proceedings of the 3rd International Conference Contaminants in Freezing Ground, Hobart 14-18 April 2002 Figures 1 and 2 in this paper are available from the SCAR Map Catalogue and have catalogue numbers 12930 and 12931.",
         "license": "proprietary"
     },
-    {
-        "id": "stem-and-branchwood-data-swiss-nfi_1.0",
-        "title": "Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083217-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083217-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D/stem-and-branchwood-data-swiss-nfi_1.0",
-        "description": "In the Swiss National Forest Inventory (NFI) the volume of the stem and of large (\u2265 7cm in diameter) and small branches is estimated based on allometric functions. These functions were developed based on data collected within the permanent plot network of the Experimental Forest Management (EFM) sites at WSL (David I. Forrester; Hubert Schmid; Jens Nitzsche (2021). The Experimental Forest Management network. EnviDat. doi: 10.16904/envidat.213). The data  were converted to digital format in two separate steps in the mid-1970s for stemwood and the mid-1980s for branchwood.  The dataset on stemwood volume contains 38\u2019864 single tree data for the mean crosswise diameter at two meter sections along the stem plus an additional measurements at 1.3 m (i.e. DBH) where the diameter is greater than or equal to 7 cm (i.e. threshold of merchantable wood) and the lengths of the stem from the base to the threshold of merchantable wood and to the tree top. The measurements were collected on 768 EFM sites in the period 1888 to 1974. The dataset on branchwood is based on a subset of the stemwood data and contains in the raw format information on 14'712 single trees. It includes aggregated data from the stemwood dataset, i.e. the DBH, the stem-diameter at 7 m from the base, and the tree height from the base to the top, as well as the measured volume of large and small branches.  In 2022, the metadata of both datasets were checked, values were examined for plausibility and duplicated entries. Duplicates were removed as far as possible and the branchwood volume data were appended to the stemwood dataset to obtain a final, single file with matching single tree data. Following this evaluation the final dataset consisted of a total of 38\u2019841 trees including 14\u2019038 trees with measured branchwood data.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stem_count_of_young_forest-191_1.0",
-        "title": "Stem count of young forest",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816843-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816843-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stem_count_of_young_forest-191_1.0",
-        "description": "# 191#  Number of regeneration trees  starting at 10 cm tall up to 11.9 cm dbh recorded in NFI\u2019s regeneration survey.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "stem_number-73_1.0",
-        "title": "Stem number",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816985-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816985-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stem_number-73_1.0",
-        "description": "Number of stems of living trees and shrubs (standing and lying)  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "stem_number_of_dead_wood-116_1.0",
-        "title": "Stem number of dead wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817146-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817146-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stem_number_of_dead_wood-116_1.0",
-        "description": "Number of stems of dead trees and shrubs (standing and lying)  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "stem_number_of_dead_wood_nfi1-248_1.0",
-        "title": "Stem number of dead wood NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817296-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817296-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stem_number_of_dead_wood_nfi1-248_1.0",
-        "description": "Number of stems of dead trees and shrubs (standing and lying)  starting at 12 cm recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "stillberg-climate_1.0",
-        "title": "Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.86716, 46.773573, 9.86716, 46.773573",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817441-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817441-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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%3D%3D/stillberg-climate_1.0",
-        "description": "# Important  This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry \"Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l..\" The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg>  and with this DOI <https://doi.org/10.16904/envidat.396>.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stillberg-reforestation_1.0",
-        "title": "Long-term treeline research dataset at Stillberg, Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.86716, 46.773573, 9.86716, 46.773573",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817572-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817572-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibGlua2VkIHdhdGVyIGRpc2NoYXJnZSwgYmVkbG9hZCB0cmFuc3BvcnQgYW5kIGJlZHJvY2sgZXJvc2lvbiBkYXRhIHNldCBpbiAxbWludXRlIHJlc29sdXRpb25cIixcIkVOVklEQVRcIixcImxpbmtlZC1kaXNjaGFyZ2UtYmVkbG9hZC10cmFuc3BvcnQtYW5kLWJlZHJvY2stZXJvc2lvbi1kYXRhLXNldFwiLFwiMS4wXCIsMjc4OTgxNTM2MCw3XSIsInVtbSI6IltcImxpbmtlZCB3YXRlciBkaXNjaGFyZ2UsIGJlZGxvYWQgdHJhbnNwb3J0IGFuZCBiZWRyb2NrIGVyb3Npb24gZGF0YSBzZXQgaW4gMW1pbnV0ZSByZXNvbHV0aW9uXCIsXCJFTlZJREFUXCIsXCJsaW5rZWQtZGlzY2hhcmdlLWJlZGxvYWQtdHJhbnNwb3J0LWFuZC1iZWRyb2NrLWVyb3Npb24tZGF0YS1zZXRcIixcIjEuMFwiLDI3ODk4MTUzNjAsN10ifQ%3D%3D/stillberg-reforestation_1.0",
-        "description": "# Important  This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry \"Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland.\" The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg>  and with this DOI 10.16904/envidat.397.",
-        "license": "proprietary"
-    },
     {
         "id": "stillwell_geology_gis_1",
         "title": "Geology of the Stillwell Hills GIS Dataset",
@@ -243879,32 +234766,6 @@
         "description": "The Stillwell Hills region comprises granulite-facies gneisses which record evidence for multiple episodes of deformation and metamorphism spanning more than 2500 million years. The predominant orthogneiss package (Stillwell Orthogneiss) is thought to represent the margin of an Archaean craton exposed in Enderby Land, some 150 km to the west that was reworked during the late Proterozoic. Younger additions to the crust include Palaeoproterozoic charnockitic gneiss (Scoresby Charnockite) and Meso-Neoproterozoic mafic sills and dykes (Point Noble Gneiss, Kemp Dykes) and felsic pegmatites (Cosgrove Pegmatites). Subordinate supracrustal rocks, including metaquartzite, metapelitic, metapsammitic and calc-silicate gneiss (Dovers Paragneiss, Sperring Paragneiss, Stefansson Paragneiss, Keel Paragneiss, Ives Paragneiss) are intercalated and infolded with the Archaean-Palaeoproterozoic orthogneisses.  This Dataset is derived from the map product 'The Geology of the Stillwell Hills, Antarctica'.  This metadata record was created using information in Geoscience Australia's metadata record at  http://www.ga.gov.au/metadata-gateway/metadata/record/78535/",
         "license": "proprietary"
     },
-    {
-        "id": "streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0",
-        "title": "Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.7178659, 47.0380179, 8.7178659, 47.0380179",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817707-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817707-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZGVhZHdvb2QgZ2VuZXJhdG9yXCIsXCJFTlZJREFUXCIsXCJkZWFkd29vZC1nZW5lcmF0b3JcIixcIjEuMFwiLDMyMjYwODE1NTEsMl0iLCJ1bW0iOiJbXCJkZWFkd29vZCBnZW5lcmF0b3JcIixcIkVOVklEQVRcIixcImRlYWR3b29kLWdlbmVyYXRvclwiLFwiMS4wXCIsMzIyNjA4MTU1MSwyXSJ9/streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0",
-        "description": "This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:     - Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);   - Rainfall at 5-minute resolution (mm per hour);   - Rainfall deuterium composition (\u2030);   - Stormflow deuterium composition (\u2030);   - Groundwater and baseflow deuterium compositions (\u2030).     For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions (\"Deuterium_Rainfall\" and \"Deuterium_Streamwater\"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values (\"Deuterium_Snapshot\") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).     More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.",
-        "license": "proprietary"
-    },
-    {
-        "id": "stumps-as-a-dead-wood-resource_1.0",
-        "title": "Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817823-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817823-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/stumps-as-a-dead-wood-resource_1.0",
-        "description": "Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.  The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.    The study is published in \ufeffAnnals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).  The data can be obtained from the authors upon reasonable request.",
-        "license": "proprietary"
-    },
     {
         "id": "sua_pan_lai_fpar_778_1",
         "title": "SAFARI 2000 LAI and FPAR Measurements at Sua Pan, Botswana, Dry Season 2000",
@@ -243970,32 +234831,6 @@
         "description": "Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).",
         "license": "proprietary"
     },
-    {
-        "id": "survey-energy-transition-municipal-level-switzerland_1.0",
-        "title": "Implementing the energy transition at municipal level in Switzerland: A regional survey",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "7.5270081, 46.8734277, 7.7714539, 47.0383456",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083086-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083086-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiaW1wYWN0IG9mIG5vbi1uYXRpdmUgdHJlZSBzcGVjaWVzIGluIGV1cm9wZSBvbiBzb2lsIHByb3BlcnRpZXMgYW5kIGJpb2RpdmVyc2l0eTogYSByZXZpZXdcIixcIkVOVklEQVRcIixcImltcGFjdC1vZi1ub24tbmF0aXZlLXRyZWUtc3BlY2llcy1pbi1ldXJvcGUtb24tc29pbC1wcm9wZXJ0aWVzLWFuZC1iaW9kaXZlcnNpdHlcIixcIjEuMFwiLDMyMjYwODI1NzgsMl0iLCJ1bW0iOiJbXCJpbXBhY3Qgb2Ygbm9uLW5hdGl2ZSB0cmVlIHNwZWNpZXMgaW4gZXVyb3BlIG9uIHNvaWwgcHJvcGVydGllcyBhbmQgYmlvZGl2ZXJzaXR5OiBhIHJldmlld1wiLFwiRU5WSURBVFwiLFwiaW1wYWN0LW9mLW5vbi1uYXRpdmUtdHJlZS1zcGVjaWVzLWluLWV1cm9wZS1vbi1zb2lsLXByb3BlcnRpZXMtYW5kLWJpb2RpdmVyc2l0eVwiLFwiMS4wXCIsMzIyNjA4MjU3OCwyXSJ9/survey-energy-transition-municipal-level-switzerland_1.0",
-        "description": "The dataset contains data from a survey, which was conducted in a periurban region close to Berne, Switzerland. The survey was conducted in Fall 2018 and contained opinion questions about the energy transition. Additionally, spatial data was collected using a PPGIS. While the opinion data is included in the data set, the spatial data is not. For more explanation, please consider the information sheet, the related publications or to contact the authors.",
-        "license": "proprietary"
-    },
-    {
-        "id": "survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0",
-        "title": "Survey of spruce seed and cone insects in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817908-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817908-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0",
-        "description": "In 1989 a nation-wide survey on spruce seed and cone insects was carried out at 29 locations distributed across the 5 main geographic regions of Switzerland. The cones were incubated in a controlled environment chamber and the emerging insects were collected and identified. The cones were kept for three years to allow diapausing insects to emerge. The methods are described in more detail in the corresponding publications.",
-        "license": "proprietary"
-    },
     {
         "id": "survey_1997_V3_1",
         "title": "Macquarie Island Mapping Program Survey Field Work and Report Voyage 3 Round Trip November 1997",
@@ -244074,162 +234909,6 @@
         "description": "Voyage 6 of the Australian Antarctic Program for 2002/03 resupplied the station at Macquarie Island. Five days were spent at the island:  March 24 - 28. During this time some surveys were carried out by Paul Boland  (DPIWE, Tasmania) and David Smith (Australian Antarctic Data Centre).     Tasks carried out by Paul included the following:    (i) Detailed surveys of the huts and other infrastructure (eg generator  platforms, walkways) at Green Gorge and Bauer Bay, a nearby stream at Green  Gorge, nearby walking tracks at Bauer Bay. This work was done for Henk Brolsma  (AAD Mapping Officer).  Such features can potentially be used to rectify aerial photographs or satellite images. The Bauer Bay data have been used to rectify a Digital Globe satellite image of north-western Macquarie Island. Refer to the metadata  record with ID macquarie_quickbird_mosimage. A survey mark was also  established near the Bauer Bay hut.    (ii) A vegetation survey at Handspike Corner for Dr Dana Bergstrom (AAD RISCC program).   (iii) A survey of the old tip site, the Power House and the bunds at the  station for Dr Ian Snape and Dr John Rayner (AAD Human Impacts Program).    The data resulting from the surveys are available for downloading from three related URLs below.  1. The dxf file, spreadsheet with levelling data and annotated photos provided by Paul; 2. Shapefiles created from the point data in the dxf file and stored in the horizontal datum ITRF2000@2000 used by Paul (note: the dxf file needs to be referred to for descriptions of the points); 3. Shapefiles representing the features surveyed at the station for the Human Impacts Program, created from the point data in the dxf file and stored in the horizontal datum WGS84.   The transformation from ITRF2000@2000 to WGS84 for this data was carried out by applying \"The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N.\" given on page 3 of the survey report \"Macquarie Island OSG  Survey Campaign, Voyage 8 Round Trip, March 2002\" by John VanderNiet and Nick Bowden.  For more information about this survey work please contact Henk Brolsma (AAD Mapping Officer).     A GPS base station was also set up for much of the resupply period with the antenna  mounted on the roof of the Biology building. Paul surveyed the antenna  position. Trimble .ssf, RINEX and .dat files were collected. This base station data and data collected by the Geoscience Australia permanent base station, MAC1, during the resupply period are available for download from a related URL below.       David used a Trimble Geoexplorer GPS to survey points at 5 metre intervals along two 50 metre transects laid out by Lee Belbin (Australian Antarctic Data Centre) near the Biology building at the station. At each point Pat Lewis (PhD  student, IASOS, University of Tasmania) collected invertebrates using a pooter for a fixed period of time while Perpetua Turner (AAD RISCC program) made notes about the vegetation and environment.  This work was done for Dr Penny Greenslade (ANU) and the samples and data were  given to her back at the AAD. The transect sample points were differentially corrected using the base station data and are available for download from a link below.  David also collected the locations of the two navigation guides on The Isthmus and Tractor Rock which is the southern extent of Station Limits on the east coast. These locations were also differentially corrected. The locations of the two navigation guides are available for download from the link below. The location of Tractor Rock is in the unofficial Australian Antarctic Gazetteer (see link below) as this name has not been approved by the Nomenclature Board of Tasmania.",
         "license": "proprietary"
     },
-    {
-        "id": "swe-measurements-gnss-along-a-steep-elevation-gradient_1.0",
-        "title": "Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.781732, 46.8296864, 9.8881513, 46.9133722",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817963-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817963-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic2hhZGluZyBieSB0cmVlcyBhbmQgZnJhY3Rpb25hbCBzbm93IGNvdmVyIGNvbnRyb2wgdGhlIHN1YmNhbm9weSByYWRpYXRpb24gYnVkZ2V0XCIsXCJFTlZJREFUXCIsXCJmb3Jlc3QtcmFkaWF0aW9uLWRhdGFcIixcIjEuMFwiLDI3ODk4MTUyNzIsN10iLCJ1bW0iOiJbXCJzaGFkaW5nIGJ5IHRyZWVzIGFuZCBmcmFjdGlvbmFsIHNub3cgY292ZXIgY29udHJvbCB0aGUgc3ViY2Fub3B5IHJhZGlhdGlvbiBidWRnZXRcIixcIkVOVklEQVRcIixcImZvcmVzdC1yYWRpYXRpb24tZGF0YVwiLFwiMS4wXCIsMjc4OTgxNTI3Miw3XSJ9/swe-measurements-gnss-along-a-steep-elevation-gradient_1.0",
-        "description": "This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS)  and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46\u00b049\u201947\u2019\u2019 N, 9\u00b048\u201934\u2019\u2019E), Laret (1515 m asl, . 46\u00b050\u20192\u2019\u2019N, 9\u00b052\u201917\u2019\u2019E), Klosters (1200 m asl, 46\u00b051\u201949\u2019\u2019N, 9\u00b053\u201917\u2019\u2019E), and K\u00fcblis (815 m asl, 46\u00b054\u201948\u2019\u2019N, 9\u00b046\u201954\u2019\u2019E).",
-        "license": "proprietary"
-    },
-    {
-        "id": "swe2hs-calibration-and-validation-data_1.0",
-        "title": "SWE2HS model calibration and validation data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.3723145, 44.9790475, 13.4802246, 47.9298398",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083104-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083104-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swe2hs-calibration-and-validation-data_1.0",
-        "description": "The data in this repository was used for the calibration and validation of the  SWE2HS model in the following publication:  Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258  Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss-biomass-potentials_1.0",
-        "title": "Potentials of domestic biomass resources for the energy transition in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818004-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818004-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/swiss-biomass-potentials_1.0",
-        "description": "Switzerland has a reliable and cost efficient energy system. Due to phase  out of nuclear energy it is necessary to find new options to maintain this powerful energy system. The Swiss energy strategy 2050 aims to reduce CO2-emissions, increase efficiency and promote renewable energies. The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) examined relevant woody and non-woody biomass quantities (cubic meters, fresh-, dry weight) and their energy potentials (in Petajoules: primary energy and biomethane) with a similar methodological approach. The work was done within the frame of the Swiss Competence Centers for Energy Research (SCCER) especially in line with the SCCER Biomass for Swiss energy future (Biosweet). With a uniform and consistent approach for the current potentials ten biomass categories were estimated and aggregated for the whole of Switzerland. In this context solutions for the technical, social and political challenges are promoted. First, considering the different biomass resources characteristics and available data, appropriate methods at the finest scale possible were elaborated to estimate the annual quantities which could theoretically be collected (theoretical potential). Then, explicit and rational restrictions for sustainable bio-energy production were defined according to the current state of the art and subtracted from the theoretical potential to obtain the sustainable potential. The main restrictions are competing material utilizations, environmental factors and supply costs. Finally, the additional sustainable potential was estimated considering the current bioenergy production. Our main purpose was to provide potentials for developing conversion technologies as well as a detailed and comprehensive basis of the Swiss biomass potentials for energy use for economic and political decision makers. The complete report is available under https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A13277/datastream/PDF/view",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss-canopy-crane-ii-research-site_1.0",
-        "title": "Swiss Canopy Crane II research site",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.7762604, 47.4387988, 7.7762604, 47.4387988",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818064-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818064-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swiss-canopy-crane-ii-research-site_1.0",
-        "description": "![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg \"Swiss Canopy Crane II\")    This research site is located near H\u00f6lstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss-fluxnet-davos_1.0",
-        "title": "Swiss FluxNet Site Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.8559167, 46.8153333, 9.8559167, 46.8153333",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083201-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083201-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swiss-fluxnet-davos_1.0",
-        "description": "The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN).   Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method.   In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.    __Measurements__    - Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.  - Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).  - Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.  - Auxiliary micrometeorology and soil climate measurements.    __Data availability__    Near real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.    __Data policy__    ICOS data license:   [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)    __Detailed site info__:   [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss-fluxnet-lageren_1.0",
-        "title": "Swiss FluxNet Site L\u00e4geren",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.364389, 47.478333, 8.364389, 47.478333",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083256-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083256-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swiss-fluxnet-lageren_1.0",
-        "description": "The Swiss FluxNet Site L\u00e4geren is a managed mixed deciduous mountain forest located on the steep L\u00e4geren mountain (NW of Zurich, Swiss Plateau). The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and the National Air Pollution Monitoring Network (NABEL). In addition to Swiss FluxNet, the site is part of the Long-term Forest Ecosystem Research (LWF) of WSL and the biological drought and growth indicator network (TreeNet) of WSL.        __Measurements__    - Ecosystem flux measurements of CO2, H2O vapour are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.  - Sub-canopy eddy fluxes (CO2, H2O), soil respiration campaigns  - Continuous CO2 profile measurements.  - Auxiliary micrometeorology and soil climate measurements.    __Data availability__    All data are available from the European Fluxes Database Cluster, but are also part of Fluxnet2015 dataset.    __Data policy__    ICOS data license:   [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)    __Detailed site info__:   [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae//](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss-municipalities-survey-on-spatial-planning-instruments_1.0",
-        "title": "Swiss Municipal Spatial Planning Survey",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817630-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817630-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swiss-municipalities-survey-on-spatial-planning-instruments_1.0",
-        "description": "Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss_landscape_services_change_1.0",
-        "title": "Land use projections and services for Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817302-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817302-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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_landscape_services_change_1.0",
-        "description": "Data and scripts of publication:    Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Lo\u00efc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.    Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.     Data Folder:  Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored    Scripts Folder:  Contains scripts organized into subfolders depending on their purpose    Note: Some abbreviations within the scripts and data are derived from German words and not English.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swiss_lulc_forecast_21th_century_1.0",
-        "title": "High resolution land use forecasts for Switzerland in the 21st century",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083049-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083049-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZ2NvcyBzd2UgZGF0YSBmcm9tIDExIHN0YXRpb25zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJnY29zLXN3ZS1kYXRhXCIsXCIxXCIsMjc4OTgxNTE2Miw3XSIsInVtbSI6IltcImdjb3Mgc3dlIGRhdGEgZnJvbSAxMSBzdGF0aW9ucyBpbiBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiZ2Nvcy1zd2UtZGF0YVwiLFwiMVwiLDI3ODk4MTUxNjIsN10ifQ%3D%3D/swiss_lulc_forecast_21th_century_1.0",
-        "description": "We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using >200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC  maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swissfungi-distribution-of-fungi-in-switzerland_1.0",
-        "title": "SwissFungi - Records Database for the Fungi of Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817130-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817130-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swissfungi-distribution-of-fungi-in-switzerland_1.0",
-        "description": "This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called  [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.    The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.    The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.    License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.",
-        "license": "proprietary"
-    },
-    {
-        "id": "swisslichens_1.0",
-        "title": "SwissLichens - Distribution of lichens in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817454-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817454-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/swisslichens_1.0",
-        "description": "This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.    The SwissLichens database currently contains more than 120\u2019000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.    Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available.  They are free of charge for research projects and can be requested from InfoSpecies using a form.    Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.",
-        "license": "proprietary"
-    },
-    {
-        "id": "synchrony_spongymoth_budburst_1.0",
-        "title": "Synchrony between spongy moth hatching and leaf phenology of temperate trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083070-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083070-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/synchrony_spongymoth_budburst_1.0",
-        "description": "The files correspond to the data and R-script used for the analyses of the following paper \"Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate\" published in Ecology and Evolution by Vitasse et al. 2023.  There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses.    Input data:  'Synchrony_Cuttings_Pheno.txt':",
-        "license": "proprietary"
-    },
     {
         "id": "sys_etm_Not provided",
         "title": "Landsat 7 ETM+ Systematically Corrected (1999-May 2003)",
@@ -245296,32 +235975,6 @@
         "description": "Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA.  ",
         "license": "proprietary"
     },
-    {
-        "id": "temperature-dependent-life-history-ips-typographus_1.0",
-        "title": "Temperature-dependent development and oviposition of the spruce bark beetle Ips typographus",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "8.4557819, 47.3611898, 8.4557819, 47.3611898",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817829-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817829-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/temperature-dependent-life-history-ips-typographus_1.0",
-        "description": "Ips typographus was reared in climate chambers at constant temperatures of 12, 15, 20, 25, 30 and 33\u00b0C. Developmental times from egg to teneral beetle stages and daily oviposition of females from preoviposition phase to their death were recorded. From these data life tables were computed and the data were used for modelling.",
-        "license": "proprietary"
-    },
-    {
-        "id": "terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0",
-        "title": "Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "21.87, -70.5, 21.87, -70.5",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817876-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817876-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0",
-        "description": "Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.    Notes:    * All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).    * From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.    * The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.    * By walking along fixed corridors, disturbance of the snow was limited.",
-        "license": "proprietary"
-    },
     {
         "id": "tf01soil_511_1",
         "title": "BOREAS TF-01 SSA-OA Soil Characteristics Data",
@@ -246115,58 +236768,6 @@
         "description": "Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.",
         "license": "proprietary"
     },
-    {
-        "id": "the-experimental-forest-management-network_1.0",
-        "title": "The Experimental Forest Management network",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817900-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817900-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/the-experimental-forest-management-network_1.0",
-        "description": "The EFM network is one of the longest running scientific projects in Switzerland and has been collecting growth and yield data since the late 1880\u2019s. As of 2021, 28 plots had been monitored for at least 100 years and 81 for at least 75 years. The network is used to examine silvicultural treatments across a range of species, climate and edaphic conditions. There are currently 465 plots covering a total area of 148 hectares. Over the > 130-year history of the project, at least another 1000 plots were monitored and then deactivated after they reached their experimental goal (e.g. end of the rotation). The data from all 1480 plots are available for analyses.",
-        "license": "proprietary"
-    },
-    {
-        "id": "the-origin_1.0",
-        "title": "The origin of urban communities: from the regional species pool to community assemblages in city",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.4771538, 47.3551798, 8.5959435, 47.4070207",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817936-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817936-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/the-origin_1.0",
-        "description": "Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland.    Dataset available upon request (An agreement between the data provider and the data recipient is necessary).",
-        "license": "proprietary"
-    },
-    {
-        "id": "the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0",
-        "title": "The usage of landscape ecological concepts in the planning literature",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "180, -90, -180, 90",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817969-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817969-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0",
-        "description": "Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.    The present dataset is part of the published scientific paper entitled \u201cLandscape ecological concepts in planning: review of recent developments\u201d (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?     We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015\u20132019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the \u201cpdfsearch\u201d package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:     &nbsp; 1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.    &nbsp; 2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.    &nbsp; 3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).   This data was used to produce the Figure 3a at the original publication.    &nbsp; 4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.       To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material \u2013 4 Integration of landscape ecological concepts into planning), and we provided two outputs:    &nbsp; 5. Planning steps.  This data provides the number of times landscape ecological concepts were addressed in each   planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the   original publication.    &nbsp; 6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the   data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the   paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning   steps described above?",
-        "license": "proprietary"
-    },
-    {
-        "id": "three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6",
-        "title": "Three-dimensional debris flow simulation tool debrisInterMixing",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818024-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818024-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x%3B%206",
-        "description": "Here the updated versions of debrisInterMixing are provided for download. The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., H\u00fcrlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017.  DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter.  For more information, please contact albrecht.vonboetticher@wasserbau.ch.     For a recent summary on applications see the DFHM8 contribution at https://www.e3s-conferences.org/articles/e3sconf/abs/2023/52/e3sconf_dfhm82023_02024/e3sconf_dfhm82023_02024.html - DOI: https://doi.org/10.1051/e3sconf/202341502024    UPDATE: DebrisInterMixing for OpenFOAM-7 is available, please contact albrecht.vonboetticher@wasserbau.ch. DebrisInterMixing with OpenFOAM-10 is ready but not yet validated.",
-        "license": "proprietary"
-    },
     {
         "id": "timber_125_1",
         "title": "Timber Measurements (OTTER)",
@@ -246180,19 +236781,6 @@
         "description": "Height, crown width, DBH, and height-to-crown distance collected using variable-radius plot sampling with steel tape and hand-held compass to locate points along transect",
         "license": "proprietary"
     },
-    {
-        "id": "time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0",
-        "title": "Time series data on dynamic crack propagation in long propagation saw tests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.869982, 46.8077169, 9.869982, 46.8077169",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083229-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083229-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0",
-        "description": "This data set includes material and results described in the related research article:    Bergfeld, B., van Herwijnen A., Bobillier, G., Rosendahl P., Wei\u00dfgraeber P., Adam V., Dual, J., and Schweizer, J.: Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and  sustained propagation, Natural Hazards and Earth System Sciences, 23, 293-315, https://doi.org/10.5194/nhess-23-293-2023, 2023.    We performed a series of propagation saw test experiments, up to ten meters long, over a period of 10 weeks and analyzed these using digital image correlation techniques. We derived the elastic modulus of the slab, the elastic modulus of the weak layer and the specific fracture energy of the weak layer with a homogeneous and a layered slab model. During crack propagation, we measured crack speed, touchdown distance and the energy dissipation due to compaction and dynamic fracture. Our data set provides unique insight and valuable data to validate models.",
-        "license": "proprietary"
-    },
     {
         "id": "tims0bil_282_1",
         "title": "BOREAS Level-0 TIMS Imagery: Digital Counts in BIL Format",
@@ -246232,149 +236820,6 @@
         "description": "The TRMM Microwave Imager (TMI) Wentz Ocean Products dataset used the TRMM Microwave Imager (TMI), which is a 5-channel, dual-polarized, passive microwave radiometer. The TMI is used to measure several important meteorological parameters over sea surfaces, such as precipitation rate, wind speed, wapter vapor, and sea surface temperature. The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz. It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.",
         "license": "proprietary"
     },
-    {
-        "id": "topoclim-v-1-0-code_1.0",
-        "title": "TopoCLIM v1.0 code",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818149-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818149-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/topoclim-v-1-0-code_1.0",
-        "description": "Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.    A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.    License GPL v.3",
-        "license": "proprietary"
-    },
-    {
-        "id": "topoclim-v1-1-code_1.1",
-        "title": "TopoCLIM v1.1 code",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816880-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789816880-ENVIDAT.html",
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-        "description": "Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.    A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.",
-        "license": "proprietary"
-    },
-    {
-        "id": "torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0",
-        "title": "Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "7.1740723, 44.2197466, 9.4592285, 46.6099536",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817001-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817001-ENVIDAT.html",
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-        "description": "This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.    The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.    The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.    These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_basal_area-2_1.0",
-        "title": "Total basal area",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817158-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817158-ENVIDAT.html",
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-        "description": "Sum of the stem cross-section areas of all living and dead trees and shrubs starting at 12 cm dbh at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_basal_area_nfi1-238_1.0",
-        "title": "Total basal area NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817277-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817277-ENVIDAT.html",
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-        "description": "Sum of stem cross-section areas at a height of 1.3 m (dbh measurement height) of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method.  In NFI1 only those dead trees were recorded whose wood could still be exploited.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_stem_number-3_1.0",
-        "title": "Total stem number",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817428-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817428-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/total_stem_number-3_1.0",
-        "description": "Number of stems of all living and dead trees and shrubs starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_stem_number_by_cause_of_damage-218_1.0",
-        "title": "Total stem number by cause of damage",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817559-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817559-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/total_stem_number_by_cause_of_damage-218_1.0",
-        "description": "Number of all living and dead trees and shrubs  starting at 12 cm dbh where a particular cause of damage (including no damage, dead or lying) was determined. One tree may have damage with more than one type of origin, which means it may contribute to the total number of stems with damage with several different types of origin.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_stem_number_by_type_of_damage-208_1.0",
-        "title": "Total stem number by type of damage",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817706-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817706-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3dpc3NsaWNoZW5zIC0gZGlzdHJpYnV0aW9uIG9mIGxpY2hlbnMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInN3aXNzbGljaGVuc1wiLFwiMS4wXCIsMjc4OTgxNzQ1NCw3XSIsInVtbSI6IltcInN3aXNzbGljaGVucyAtIGRpc3RyaWJ1dGlvbiBvZiBsaWNoZW5zIGluIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCJzd2lzc2xpY2hlbnNcIixcIjEuMFwiLDI3ODk4MTc0NTQsN10ifQ%3D%3D/total_stem_number_by_type_of_damage-208_1.0",
-        "description": "Number of all living and dead trees and shrubs  starting at 12 cm dbh where a particular type of damage (including no damage, dead or lying) was observed. One tree may have more than one type of damage, which means it may contribute to the total number of stems for several different types of damage.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_stem_number_nfi1-243_1.0",
-        "title": "Total stem number NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817822-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817822-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/total_stem_number_nfi1-243_1.0",
-        "description": "Number of stems of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_timber_volume-23_1.0",
-        "title": "Total timber volume",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817860-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817860-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/total_timber_volume-23_1.0",
-        "description": "Volume of stemwood with bark of all living and dead trees and shrubs (standing and lying)  starting at 12 cm dbh. This corresponds to the sum of the volumes of growing stock and deadwood. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "total_timber_volume_nfi1-242_1.0",
-        "title": "Total timber volume NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817882-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817882-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/total_timber_volume_nfi1-242_1.0",
-        "description": "Volume of stemwood with bark of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "trace_metals_1",
         "title": "Iron, manganese and aluminium in East Antarctic snow and ice cores",
@@ -246401,45 +236846,6 @@
         "description": "The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa for the period August 14, 2000 to September 23, 2000. These sites are Durban, Middleburg, Pietersburg, and Springbok, South Africa; Maun, Botswana; Mongu, Zambia; and Windhoek, Namibia. The twice daily three-dimensional wind field (at 0000 and 1200 UTC) was used as input to the trajectory model. By integrating the vertical motion of the air parcels over a period of time, the trajectory model was able to forecast the net vertical displacement of air parcels during 12-hour periods. The resulting trajectory plots represent the three-dimensional transport of air in time and can be used to examine what is happening in the low-to-mid troposphere during flight and ground-based observations. These levels are most significant in terms of the thermodynamic structure of the troposphere, especially the stable layers and accumulation of material between and below them, as well containing the major levels of subsidence over the subcontinent. The trajectory model output and  thermodynamic profiles of the troposphere were used to position aircraft for sampling trace gases, aerosols and other species during the SAFARI 2000 field campaign and to predict regions of high aerosol and trace gas concentrations downwind.The model output data are daily forward and backward trajectory plots at 850 hPa, 700 hPa, and 500 hPa pressure levels for each location. The plots are provided as JPEG images with coordinate, date, and time stamps.",
         "license": "proprietary"
     },
-    {
-        "id": "tree-ring-data-earlybrowning-2018_1.0",
-        "title": "Tree-ring data of European beech with premature leaf discoloration in 2018 and beech with normal leaf fall",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "7.2756958, 47.1903969, 9.2092896, 47.7990732",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083107-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083107-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/tree-ring-data-earlybrowning-2018_1.0",
-        "description": "Tree-ring data and tree location from 470 European beech trees (Fagus sylvatica L.) located in the northern part of Switzerland.  278 trees showed drought-induced premature leaf discoloration and shedding in summer 2018 and 192 showed normal leaf fall. The trees were selected from the \"1000-Beech-Project\" published by Frei et al. 2022  and the data was analyzed in Neycken et al 2023 (in preparation). The corresponding crown data are archived in the EnviDat data portal https://doi.org/10.16904/envidat.422 (Frei et al. 2023). All other data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.    Publications related to original data set and crown data:    Wohlgemuth, T., Kistler, M., Aymon, C., Hagedorn, F., Gessler, A., Gossner, M.M., Queloz, V., V\u00f6gtli, I., Wasem, U., Vitasse, Y., Rigling, A., 2020. Fr\u00fcher Laubfall der Buche w\u00e4hrend der Sommertrockenheit 2018: Resistenz oder Schw\u00e4chesymptom? Schweizerische Zeitschrift fur Forstwesen 171, 257\u2013269. https://doi.org/10.3188/szf.2020.0257    Frei, E.R., Gossner, M.M., Vitasse, Y., Queloz, V., Dubach, V., Gessler, A., Ginzler, C., Hagedorn, F., Meusburger, K., Moor, M., Sambl\u00e1s Vives, E., Rigling, A., Uitentuis, I., von Arx, G., Wohlgemuth, T., 2022. European beech dieback after premature leaf senescence during the 2018 drought in northern Switzerland. Plant Biol J 24, 1132\u20131145. https://doi.org/10.1111/plb.13467      Publication related to tree-ring data and growth analysis:  Neycken et al 2023 (in preparation)",
-        "license": "proprietary"
-    },
-    {
-        "id": "tree-ring-laser-ablation-data_1.0",
-        "title": "Tree-ring laser ablation data",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "125.8374023, 45.0507576, 128.5620117, 46.46107",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083113-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083113-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/tree-ring-laser-ablation-data_1.0",
-        "description": "This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.    In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which  derived from air pollution and may be used as indicators over the period 1965\u20132020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.    Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.",
-        "license": "proprietary"
-    },
-    {
-        "id": "tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0",
-        "title": "Tree rings and climate data of four tree species in Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817967-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817967-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0",
-        "description": "Raw tree ring data and climate used in the following paper:  Vitasse Y, Bottero A, Cailleret M et al. (2019) Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob Chang Biol, 25, 3781-3792.",
-        "license": "proprietary"
-    },
     {
         "id": "tree_cover-1km_641_1",
         "title": "SAFARI 2000 Tree Cover from AVHRR, 1-km, 1992-1993 (DeFries et al.)",
@@ -246466,19 +236872,6 @@
         "description": "Measurements from Indonesia made during 1998.",
         "license": "proprietary"
     },
-    {
-        "id": "trichopria_drosophilae_nuclear_microsats_1.0",
-        "title": "Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.4492188, 45.4870947, 11.0522461, 48.1770756",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818012-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818012-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIiwidW1tIjoiW1wibXBzLTIgc29pbCB3YXRlciBtYXRyaWMgcG90ZW50aWFsIGx3ZlwiLFwiRU5WSURBVFwiLFwiZW52aWRhdC1sd2YtMzFcIixcIjIwMTktMDMtMDZcIiwyNzg5ODE1MTU5LDddIn0%3D/trichopria_drosophilae_nuclear_microsats_1.0",
-        "description": "# Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_    This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).  Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.",
-        "license": "proprietary"
-    },
     {
         "id": "trichototo_0",
         "title": "Trichodesmium Toto (TRICHOTOTO) cruise",
@@ -246518,45 +236911,6 @@
         "description": "The TRMM Cyclone Precipitation Feature (TCPF) Database - Level 1 provides Tropical Rainfall Measuring Mission (TRMM)-based tropical cyclone data in a common framework for hurricane science research. This dataset aggregated observations from each of the TRMM instruments for each satellite orbit that was coincident with a tropical cyclone in any of the six TC-prone ocean basins. These swath data were co-located and subsetted to a 20-degree longitude by 20-degree latitude bounding box centered on the tropical storm, which is typically large enough to observe the various sizes of TCs and their immediate environments. The TCPF Level 1 dataset was created by researchers at Florida International University (FIU) and the University of Utah (UU) from the UU TRMM Precipitation Feature database. The TCPF database was built by extracting those precipitation features that are identified as tropical cyclones (TC) using the TC best-track data provided by National Hurricane Center or the US Navy's Joint Typhoon Warning Center.",
         "license": "proprietary"
     },
-    {
-        "id": "tschamut2014_1.0",
-        "title": "Repetitive trajectory testing in Tschamut 2014",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "8.7003458, 46.6526428, 8.7028348, 46.6545575",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818065-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818065-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/tschamut2014_1.0",
-        "description": "In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.    An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.     The dataset contains all data that were gathered through above field campaign.",
-        "license": "proprietary"
-    },
-    {
-        "id": "turbulence-patchy-snow-cover_1.0",
-        "title": "Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "9.9224997, 46.7196131, 9.9224997, 46.7196131",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083051-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083051-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/turbulence-patchy-snow-cover_1.0",
-        "description": "This dataset contains the raw data that is analyzed in the publication entitled \"Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow\". Please find information on the individual data files in the description of the files. The data was recorded during a comprehensive field campaign in May and June 2021 at D\u00fcrrboden at the end of Dischma valley close to Davos (Graub\u00fcnden, CH).",
-        "license": "proprietary"
-    },
-    {
-        "id": "twig_mass_of_live_trees-48_1.0",
-        "title": "Twig mass of live trees",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817079-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817079-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/twig_mass_of_live_trees-48_1.0",
-        "description": "Dry weight (mass) of branches with a diameter under 7 cm from living trees and shrubs  starting at 12cm dbh.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "twin_pups_1",
         "title": "Apparent twin pups of the weddell seal near Mawson, Antarctica",
@@ -246609,32 +236963,6 @@
         "description": "The UAlbany Soundings IMPACTS dataset consists of data measured with the iMet-3050A sounding system using 200-g meteorological balloons during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The UAlbany Soundings IMPACTS dataset consists of atmospheric pressure, relative humidity, mixing ratio, wind speed, and wind direction measurements. These data are available from January 5, 2023, through March 1, 2023, in ASCII format.",
         "license": "proprietary"
     },
-    {
-        "id": "uas-based-snow-depth-maps-bramabuel-davos-ch_1.0",
-        "title": "UAS based snow depth maps Br\u00e4mab\u00fcel, Davos, CH",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2016-01-01",
-        "end_date": "2016-01-01",
-        "bbox": "9.8459816, 46.7767433, 9.8551011, 46.782768",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817199-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817199-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/uas-based-snow-depth-maps-bramabuel-davos-ch_1.0",
-        "description": "This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Br\u00e4mab\u00fcel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.    A detailed description is given here:    B\u00fchler, Y., Adams, M. S., B\u00f6sch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.    Abstract:  Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.   Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Fl\u00fcela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.",
-        "license": "proprietary"
-    },
-    {
-        "id": "uav-datasets-for-three-alpine-glaciers_1.0",
-        "title": "UAV-derived Digital Surface Models and orthoimages for three alpine glaciers",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2017-01-01",
-        "end_date": "2017-01-01",
-        "bbox": "7.8153698, 45.9888032, 8.6115619, 46.6089963",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817320-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817320-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/uav-datasets-for-three-alpine-glaciers_1.0",
-        "description": "### UAV-derived DSMs and orthoimages    Unmanned Aerial Vehicle  (UAV) surveys were conducted between 2015 and 2016 on the __Sankt Annafirn__, __Findelen-__ and __Griesgletscher__, situated in the __Swiss Alps__. Three surveys at the Sankt Annafirn allowed for a full glacier coverage,  four  surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a __high resolution orthoimage__ and a __Digital Surface Model (DSM)__ was created.    ### UAV surveys:    Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 .    The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area.    A folder for each dataset is available (see folder name in table), which contains:    - An orthoimage  __*glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*__    - A Digital Surface Model  __*glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*__    - The Agisoft Photoscan automatic processing report   __*glacier_date_photoscan_report.pdf*__    where:    - __*glacier*__ is the name of the surveyed glacier    - __*date*__ is the date of the UAV image acquisition    - __*photoscan*__ is the name of the photogrammetric software    - __*oi*__ or __*dsm*__ the type of dataset    - __*CH1903+_LV95*__ is the coordinate system and datum of the dataset    - __*0.1m*__ is the resolution of the dataset in meter    - __*.tif*__ is the extention of the dataset    &nbsp;    Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below.    __Paper Citation:__   > _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles\u2019Imagery on Glaciers, Remote Sensing, 9, 186, 1-15,  [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._    The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.",
-        "license": "proprietary"
-    },
     {
         "id": "uiucsndimpacts_1",
         "title": "Mobile UIUC Soundings IMPACTS V1",
@@ -246700,19 +237028,6 @@
         "description": "Distribution of marine turtles in the Indian Ocean. Information was obtained from published and unpublished literature, and through liaison with turtle fieldworkers. It was intended that the database would be of use to a wide audience, including biologists, coastal planners and those concerned with emergency response to oil spills.  Assessing the level of demand for these data, and the feasibility of maintaining data to reflect best available information.",
         "license": "proprietary"
     },
-    {
-        "id": "urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0",
-        "title": "Urals: latitudinal decline in treeline biomass and productivity",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "52.9101563, 46.5830301, 74.7070313, 71.42438",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817471-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817471-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0",
-        "description": "1. Stand characteristics of treeline ecotone along 18 elevational gradients of the Ural mountains.   2. Extrapolated climate data at treeline using nearby meteo station (1976-2006).  3. Air and soil temperatures measured in situ at treeline in the South and Polar Urals. Soil temperature sensors were placed at 10 cm depth in open areas in between tree clusters but not under tree canopy.  4. Further plot specific information is available upon request.",
-        "license": "proprietary"
-    },
     {
         "id": "urn:eop:VITO:CGS_S1_GRD_L1_V001",
         "title": "Sentinel-1 Level-1 Ground Range Detected (GRD) products.",
@@ -248637,19 +238952,6 @@
         "description": "Northern Prairie has a long history of studying nest success of upland nesting ducks. Over the years, we have developed standardized procedures for collecting and analyzing these types of data. Data forms and instruction manuals developed by the Center are used widely by biologists throughout the northern Great Plains and elsewhere. Extensive use of standardized procedures led to a cooperative effort among Federal, State, Private, and other Non-Government Organizations that has allowed us to compile the Nest File, a data base of more than 75,000 duck nests spanning 30+ years in the northern Great Plains.",
         "license": "proprietary"
     },
-    {
-        "id": "validation-of-the-critical-crack-length-in-snowpack_1.0",
-        "title": "Validating and improving the critical crack length in SNOWPACK",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.78797, 46.80757, 9.809407, 46.8292944",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817607-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817607-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/validation-of-the-critical-crack-length-in-snowpack_1.0",
-        "description": "To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.   These data were analyzed and results were published in Richter et al. (2019).    Please refer to the Readme file for further details on the data.    These data are the basis of the following publication:  Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353\u20133366, https://doi.org/10.5194/tc-13-3353-2019, 2019.",
-        "license": "proprietary"
-    },
     {
         "id": "vanderford_data_1983_85_1",
         "title": "Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85",
@@ -248676,19 +238978,6 @@
         "description": "A collection of gravity readings, taken on the Vanderford Glacier in February 1980. Also includes barometric pressure readings, taken at the same time, for determining the height of the location where the reading was taken.  Physical copies of these documents have been stored in the Australian Antarctic Division records store.",
         "license": "proprietary"
     },
-    {
-        "id": "vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0",
-        "title": "Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "174.375, -84.9479651, -179.546875, -42.7168763",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083103-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083103-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibWFzcyBvZiBuZWVkbGVzIG9yIGxlYXZlcyBvZiBsaXZlIHRyZWVzXCIsXCJFTlZJREFUXCIsXCJtYXNzX29mX25lZWRsZXNfb3JfbGVhdmVzX29mX2xpdmVfdHJlZXMtNDlcIixcIjEuMFwiLDI3ODk4MTYxMDQsN10iLCJ1bW0iOiJbXCJtYXNzIG9mIG5lZWRsZXMgb3IgbGVhdmVzIG9mIGxpdmUgdHJlZXNcIixcIkVOVklEQVRcIixcIm1hc3Nfb2ZfbmVlZGxlc19vcl9sZWF2ZXNfb2ZfbGl2ZV90cmVlcy00OVwiLFwiMS4wXCIsMjc4OTgxNjEwNCw3XSJ9/vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0",
-        "description": "# Summary    This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25\u00b0 x 0.25\u00b0 (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.    # Main model characteristics    The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.    ### Model Sublimation  Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.    ### Model Air Parcel  Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.    # References    Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6    Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705",
-        "license": "proprietary"
-    },
     {
         "id": "veg_continuous_fields_xdeg_931_1",
         "title": "ISLSCP II Continuous Fields of Vegetation Cover, 1992-1993",
@@ -248715,19 +239004,6 @@
         "description": "The National Drought Mitigation Center produces VegDRI in collaboration with the US Geological Survey's (USGS) Center for Earth Resources Observation and Science (EROS), and the High Plains Regional Climate Center (HPRCC), with sponsorship from the US Department of Agriculture's (USDA) Risk Management Agency (RMA). Main researchers working on VegDRI are Dr. Brian Wardlow and Dr. Tsegaye Tadesse at the NDMC, and Jesslyn Brown with the USGS, and Dr. Yingxin Gu with ASRC Research and Technology Solutions, contractor for the USGS at EROS.  VegDRI maps are produced every two weeks and provide regional to sub-county scale information about drought's effects on vegetation. In 2006, VegDRI covered seven states in the Northern Great Plains (CO, KS, MT, NE, ND, SD, and WY). It expanded across eight more states in 2007 to cover the rest of the Great Plains (NM, OK, MO, and TX) and parts of the Upper Midwest (IA, IL, MN, and WI). VegDRI expanded to include the western U.S. in 2008 (WA, ID, OR, UT, CA, AZ, NV). In May 2009 VegDRI began depicting the eastern states as well, covering the entire conterminous 48-state area.",
         "license": "proprietary"
     },
-    {
-        "id": "vegetation-height-model-nfi_2019 (current)",
-        "title": "Vegetation Height Model NFI",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.9161377, 45.7406934, 10.5743408, 47.8574029",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817832-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817832-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widG90YWwgc3RlbSBudW1iZXIgYnkgdHlwZSBvZiBkYW1hZ2VcIixcIkVOVklEQVRcIixcInRvdGFsX3N0ZW1fbnVtYmVyX2J5X3R5cGVfb2ZfZGFtYWdlLTIwOFwiLFwiMS4wXCIsMjc4OTgxNzcwNiw3XSIsInVtbSI6IltcInRvdGFsIHN0ZW0gbnVtYmVyIGJ5IHR5cGUgb2YgZGFtYWdlXCIsXCJFTlZJREFUXCIsXCJ0b3RhbF9zdGVtX251bWJlcl9ieV90eXBlX29mX2RhbWFnZS0yMDhcIixcIjEuMFwiLDI3ODk4MTc3MDYsN10ifQ%3D%3D/vegetation-height-model-nfi_2019%20(current)",
-        "description": "A national vegetation height model was calculated for Switzerland using digital aerial images.  We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution  (1 \u00d7 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual  vegetation heights using a digital terrain model (DTM)  based on laser data with the buildings masked out, and to  produce a vegetation height model (VHM). Such a model  will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very  high level of detail. For covering the whole of Switzerland,  we use summer aerial images from six years.    Latest version is from 2019.",
-        "license": "proprietary"
-    },
     {
         "id": "vegsoils_wilhend_642_1",
         "title": "SAFARI 2000 Vegetation and Soils, 1-Deg (Wilson and Henderson-Sellers)",
@@ -249001,84 +239277,6 @@
         "description": "This dataset represents Weddell Seal haulout and pupping sites in the Vestfold Hills, Antarctica. The data were sourced from a dataset compiled by Samantha Lake and described by the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'. She used a reporting grid described by the metadata record 'Weddell seal reporting grid of the Vestfold Hills, Antarctica' to show observations made over 24 years (pupping areas) and 28 years (non-breeding areas). The map Samantha produced of pupping areas is linked to the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'.  Polygons were generated by copying relevant grid rectangles from a digital version of the reporting grid, referring to the maps produced by Samantha; the grid rectangles used were those in which there had been greater than 20 observations (pupping), 17 observations (non-breeding). The data was used in an A3 map of the Vestfold Hills published by the Australian Antarctic Data Centre in October 2001 and which is available from a Related URL below.  The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.  Data described by this metadata record has Dataset_id = 155.  Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.",
         "license": "proprietary"
     },
-    {
-        "id": "vineyard-plots-in-southern-switzerland_1.0",
-        "title": "Vineyard plots in southern Switzerland",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2023-01-01",
-        "end_date": "2023-01-01",
-        "bbox": "8.3811951, 45.8221902, 9.2930603, 46.6202927",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083115-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083115-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/vineyard-plots-in-southern-switzerland_1.0",
-        "description": "Geospatial vector data (shapefile) representing the cadastral plots in the Canton Ticino and the Moesa region (southern Switzerland) having a part of the surface occupied by vineyards in the years 1989 and/or 2020 according to the corresponding edition of the Swiss national topographic maps in the scale 1:25,000 and to the topographic landscape model of Switzerland swissTLM3D (Federal office of topography Swisstopo). In the attribute table there is many variables which describe the topography of the site, the characteristics of the plots and the evolution of the wine growing area inside the plot between 1989 and 2020. Coordinate system: EPSG:2056 - Swiss CH1903+ / LV95.",
-        "license": "proprietary"
-    },
-    {
-        "id": "volume-21_1.0",
-        "title": "Volume",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817894-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817894-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/volume-21_1.0",
-        "description": "Volume of stemwood with bark of living trees and shrubs (standing and lying)  starting at 12 cm dbh. This corresponds internationally to the \"growing stock\". The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "volume_of_bole_wood_hg_2000-167_1.0",
-        "title": "Volume of bole wood (HG 2000)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817935-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817935-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/volume_of_bole_wood_hg_2000-167_1.0",
-        "description": "Wood volume of the stem without bark or stump at least 7 cm in diameter (limit of coarse wood) of all trees and shrubs  starting at 12 cm dbh, based on the stem-form functions according to Kaufmann (2001). The definition of the assortment is based on the 2000 edition of the Trading Practices (Handelsgebr\u00e4uchen Ausgabe 2000\u00a0).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "volume_of_bole_wood_hg_2010-211_1.0",
-        "title": "Volume of bole wood (HG 2010)",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817965-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817965-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/volume_of_bole_wood_hg_2010-211_1.0",
-        "description": "Wood volume of the trunk without bark or branches at least 7 cm in diameter (limit for coarse wool) of all trees and shrubs  starting at 12 cm dbh, based on the stem-form function according to Kaufmann (2001). The definition of the assortment is based on the 2010 edition of the Trading Practices (Handelsgebr\u00e4uchen Ausgabe 2010).    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "volume_of_dead_wood-24_1.0",
-        "title": "Volume of dead wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818001-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818001-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/volume_of_dead_wood-24_1.0",
-        "description": "Volume of stemwood with bark of all dead trees and shrubs (standing and lying)  starting at 12 cm dbh.  Unlike this theme\u00a0, the \"Amount of deadwood according to the method of NFI3\" includes all lying deadwood  starting at 7 cm in diameter.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "volume_of_dead_wood_nfi1-249_1.0",
-        "title": "Volume of dead wood NFI1",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818053-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818053-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/volume_of_dead_wood_nfi1-249_1.0",
-        "description": "Volume of stemwood with bark of all dead trees and shrubs (standing and lying)  starting at 12 cm dbh  recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
     {
         "id": "voyages_2",
         "title": "List of voyages and station parties between 1947 and 1989 in which Australians participated, including winter and some summer personnel",
@@ -249105,45 +239303,6 @@
         "description": "This is a collaborative proposal by Principal Investigators at the University of Washington and the Desert Research Institute. They will make detailed measurements of the temporal and spatial variations of firn compaction to advance knowledge and understanding of ice deformation and across different fields, including remote sensing, snow morphology, and paleoclimatology. They will make detailed measurements through two winter and three summer seasons at Summit Greenland using the concept of Borehole Optical Stratigraphy, which will use a borehole camera to record details of the wall. These details can be tracked over time to determine vertical motion and strain, which in the shallow depth is dominated by firn compaction. Quantitative understanding of firn compaction is important for remote-sensing mass-balance studies, which seek to measure and interpret the changing height of the ice sheet; the surface can rise due to snow accumulation, and fall due to ice flow and increased densification rates. Quantitative knowledge of all three processes is essential. Evidence suggests that the rate of densification undergoes a seasonal cycle, related to the seasonal cycle of temperature. When interpreting ice core trapped-gas data for paleoclimate, it is important to know at what point the gas was actually trapped in the ice. The pores do not close off until deep in the firn, leading to a difference between the age of the ice and the age of the trapped gas. If summer high temperatures have more impact on compaction than mean annual temperatures, the gas-age/ice-age offset might be incorrectly calculated. Greater understanding of firn densification physics will help the interpretation of these records.   This data covers accumulation rates occurring between 1980-2008, and the data were collected between 2004-2008.",
         "license": "proprietary"
     },
-    {
-        "id": "waldinventursihlwald_1.0",
-        "title": "Supplementary Data Sample Plot Inventory Sihlwald",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.552084, 47.2538697, 8.552084, 47.2538697",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818127-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818127-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wic3RhYmxlIHdhdGVyIGlzb3RvcGVzIGluIHNub3cgYW5kIHZhcG9yIG9uIHRoZSB3ZWlzc2ZsdWhqb2NoXCIsXCJFTlZJREFUXCIsXCJzdGFibGUtd2F0ZXItaXNvdG9wZXMtaW4tc25vdy1hbmQtdmFwb3Itb24tdGhlLXdlaXNzZmx1aGpvY2hcIixcIjEuMFwiLDI3ODk4MTc4OTIsN10iLCJ1bW0iOiJbXCJzdGFibGUgd2F0ZXIgaXNvdG9wZXMgaW4gc25vdyBhbmQgdmFwb3Igb24gdGhlIHdlaXNzZmx1aGpvY2hcIixcIkVOVklEQVRcIixcInN0YWJsZS13YXRlci1pc290b3Blcy1pbi1zbm93LWFuZC12YXBvci1vbi10aGUtd2Vpc3NmbHVoam9jaFwiLFwiMS4wXCIsMjc4OTgxNzg5Miw3XSJ9/waldinventursihlwald_1.0",
-        "description": "# Supplementary Data Sample Plot Inventory Sihlwald       The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label \u2018Nature discovery park\u2019 (\u2018Naturerlebnispark\u2019). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003.   This dataset contains supplementary tables for the publication by Br\u00e4ndli et al. (2020). The metadata file describes the structure of the tables.",
-        "license": "proprietary"
-    },
-    {
-        "id": "water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0",
-        "title": "Water availability of Swiss forests during the 2015 and 2018 droughts",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817096-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817096-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0",
-        "description": "The Swiss forests' water availability during the 2015 and 2018 droughts was modelled by implementing the mechanistic Soil-Vegetation-Atmosphere-Transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information and measured soil matric potential and eddy covariance data. Data include   1) csv of soil matrix potential and eddy covariance data, 2) csv of posterior model parameters, 3) geotiffs of plant-available water storage capacity until 1m soil depth and the potential rooting depth, 4) geotiffs of yearly average (2014-2019) of precipitation (P), actual evapotranspiration (ETa), evaporation as the sum of soil, snow and interception evaporation (E), actual transpiration (Ta), runoff (F) and total soil water storage (SWAT), 5) csv of simulated root water uptake aggregated for different soil depths per deciduous and coniferous trees across Switzerland at daily resolution and cumulative root fraction per soil depth for coniferous and deciduous sites, 6) geotiffs of the ratio of actual to potential transpiration (-) as mean of non-drought years 2014, 2016, 2017, 2019 and 2015 and 2018 for the month June, July, August, September and October, 7) geotiff of mean soil matric potential in the rooting zone in August 2018, 8) geotiffs of gravitational water capacity (mm) until 1 m soil depth and the maximum rooting depth (mrd), 9) geotiffs of uncertainties of the available water storage capacity (AWC) until 1m soil depth and the mean maximum rooting depth (mrd), 10) csv of average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest.",
-        "license": "proprietary"
-    },
-    {
-        "id": "water-isotopes-plynlimon_1.0",
-        "title": "Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "-3.7631607, 52.418789, -3.6402512, 52.4982845",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817232-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817232-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=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-isotopes-plynlimon_1.0",
-        "description": "The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.",
-        "license": "proprietary"
-    },
     {
         "id": "wbandimpacts_1",
         "title": "ACHIEVE W-Band Cloud Radar IMPACTS",
@@ -249157,45 +239316,6 @@
         "description": "The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format.",
         "license": "proprietary"
     },
-    {
-        "id": "weather-snowpack-danger_ratings-data_1.0",
-        "title": "Weather, snowpack and danger ratings data for automated avalanche danger level predictions",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817371-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817371-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/weather-snowpack-danger_ratings-data_1.0",
-        "description": "Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.  These data are the basis of the following publication:  P\u00e9rez-Guill\u00e9n, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., P\u00e9rez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031\u20132056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.",
-        "license": "proprietary"
-    },
-    {
-        "id": "weather-station-wolfgangpass_1.0",
-        "title": "Weather Station Davos Wolfgang",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.853594, 46.835577, 9.853594, 46.835577",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817645-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817645-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/weather-station-wolfgangpass_1.0",
-        "description": "The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).",
-        "license": "proprietary"
-    },
-    {
-        "id": "weather_station_klosters_1.0",
-        "title": "Weather Station Klosters",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.880413, 46.869019, 9.880413, 46.869019",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817512-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817512-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/weather_station_klosters_1.0",
-        "description": "A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).",
-        "license": "proprietary"
-    },
     {
         "id": "wed_sat_99_1",
         "title": "Behaviour of Weddell seals during winter recorded by satellite tracking.",
@@ -249209,84 +239329,6 @@
         "description": "This data set contains the results from a study of the behaviour of Weddell seals (Leptonychotes weddelli) at the Vestfold Hills, Prydz Bay, Antarctica. Three satellite transmitters were deployed on tagged female Weddell seals at the Vestfold Hills mid-winter (June) 1999. The transmitters were recovered in December, late in the pupping season. In total, the three transmitters were deployed and active 170 days, 175 days and 180 days. I used the first two classes of data to get fixes with a standard deviation less than 1 km. Most seal holes were more that 1 km apart (see Entry: wed_survey) so at this resolution we can distinguish between haul-out sites. We examine the number and range of locations used by the individual seals. We use all data collectively to look at diurnal and seasonal changes in haul-out bouts. None of the seals were located at sites outside the area of fast ice at the Vestfold Hills, although one seal was sighted on new fast-ice (20 - 40 cm thick). Considering the long bouts in the water, and that we only tracked haul-out locations, the results do not eliminate the possibility that the seals made long trips at sea.  The original data are stored by the Australian Antarctic Division in the ARGOS system on the mainframe Alpha. The transmitter numbers are 23453, 7074 and 7075.",
         "license": "proprietary"
     },
-    {
-        "id": "weird_1.0",
-        "title": "Quantifying Surface Heat Exchange over Heterogeneous Land Surfaces at Ultra-High Spatio-Temporal Resolution",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "9.9481845, 46.8594029, 9.9481845, 46.8594029",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817746-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817746-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIiwidW1tIjoiW1wicG9sbGluYXRpb24gZXhwZXJpbWVudDogaW5zZWN0IHRyYWl0c1wiLFwiRU5WSURBVFwiLFwicG9sbGluYXRpb24tZXhwZXJpbWVudC1pbnNlY3QtdHJhaXRzXCIsXCIxLjBcIiwyNzg5ODE2NTQ5LDddIn0%3D/weird_1.0",
-        "description": "The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high resolution thermal infrared camera pointing at synthetic screens, a 30Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen\u2019s surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind speed reflect multiple layers of different static stability within 2m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind speed profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes.  Here you find the data and the documented code used to create the plots in the publication.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wetlands-of-zurich_1.0",
-        "title": "Wetlands of the canton of Z\u00fcrich (Switzerland): Data on species richness and recent and historic area and connectivity of 55 fens",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "8.3551025, 47.0301987, 9.2120361, 47.6407189",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817856-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817856-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/wetlands-of-zurich_1.0",
-        "description": "This dataset includes data on species richness of vascular plants and bryophytes in 55 wetlands of the canton of Z\u00fcrich (Switzerland) as well as recent and historic data on the area and connectivity of these 55 wetlands and was used for the paper Jamin A., Peintinger M., Gimmi U., Holderegger R., Bergamini A. (2020) Evidence for a possible extinction debt in Swiss wetland specialist plants. Ecology and Evolution.  Species richness data are available for vascular plants and bryophytes. The field survey was carried out between June 5 and August 10, 2012. The survey covered all wetland (fen) types in the canton of Z\u00fcrich. For data collection, at least half a day per wetland was spent searching for species. Within each wetland all different vegetation types were covered until no new species were found to get as complete species lists as possible. In the Excel file information on species richness of the following groups is provided: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix 1a of the wetland inventory of Switzerland (BUWAL, 1990).   Based on the data of Gimmi et al. (2011), the area of all wetlands in 1850, 1900, 1950 and 2000 were determined as well as the wetland area within buffers 2km in radius with the center of the wetland as starting point. These data are also provided in the Excel sheet.  Moreover, for each wetland mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants were calculated and are provided in Excel sheet. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wfj-cal_1.0",
-        "title": "WFJ_CAL: Calibration dataset for snow models",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083165-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083165-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/wfj-cal_1.0",
-        "description": "During the development of DAISY, the snowpack model we  realised that we did not have enough accurate calibration measurements. We  needed more reliable measurements of snow temperatures and settlements  within the snow cover.    Therefore, from winter 1990/91 the thermal development of the season snow  cover in the test field with self-developed temperature harps was measured.  These temperature harps can move freely with the snow cover, in contrast to  the usually fixed temperature profiles.    With these harps, it became possible to monitor the temperatures and  settlements of the individual layers throughout the winter.    Additionally, the surface temperature, snow level and the usual  meteorological parameters such as air temperature, humidity, wind speed and  the radiation in various wavelength ranges were measured.    Furthermore, conventional snow profiles were recorded with measurements of  densities, hardness, grain sizes and grain shapes.    During three winters, this facility was intensively used for monitoring  purposes. The support and monitoring of these measurements and the  accompanying, very time-intensive manual measurements were carried out by  Peter Weilenmann and Franz Herzog.    The results of these measurements in winter 1990/91, 1991/92 and 1992/93  are given in the internal report No. 723.    The use of these measurements for the validation of DAISY and MiniDAISY are  gathered internally in report No. 724..726.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wfj2_1.0",
-        "title": "WFJ2: Snow measurements from the Weissfluhjoch research site, Davos",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "9.809568, 46.829598, 9.809568, 46.829598",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083117-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3226083117-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/wfj2_1.0",
-        "description": "This dataset provides HS, TSS and TS50, TS100, TS150 at the station WFJ2 situated on the Weisfluhjoch research site (2536 m asl). It has been created from merging ENET and IMIS datsets to form a continuous timeseries from 1992- present. ENET is at 1 h resolution whereas IMIS is 30 min. This is a level 2 dataset as defined [here](http://models.slf.ch/p/dataset-processing/).",
-        "license": "proprietary"
-    },
-    {
-        "id": "wfj_ice_layers_1.0",
-        "title": "WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.8093855, 46.8297006, 9.8093855, 46.8297006",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817883-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817883-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/wfj_ice_layers_1.0",
-        "description": "The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wfj_rhossa_1.0",
-        "title": "WFJ_RHOSSA: Multi-instrument stratigraphy data for the seasonal evolution of an alpine snowpack",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "9.8093934, 46.8296448, 9.8093934, 46.8296448",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817928-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817928-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/wfj_rhossa_1.0",
-        "description": "The WFJ_RHOSSA dataset contains multi-instrument, multi-resolution snow stratigraphy measurements  for the seasonal evolution of the snowpack from the Weissfluhjoch research site, Davos, Switzerland.    The measurements were initiated during the RHOSSA field campaign conducted in the winter season 2015\u20132016 with a focus on density (RHO) and specific surface area (SSA) measurements.    The Instruments and methods used in the campaign at different spatial and temporal resolution are: SnowMicroPen, Density Cutter, IceCube, Traditional profiles, Stability tests and X-ray tomography. The measurements are complemented by simulation data from the model SNOWPACK.",
-        "license": "proprietary"
-    },
     {
         "id": "white_model_parameters_652_1",
         "title": "Literature-Derived Parameters for the BIOME-BGC Terrestrial Ecosystem Model",
@@ -249339,19 +239381,6 @@
         "description": "This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10 N to 25 S, 30 to 85 W. The data are in ASCII GRID format. The data consist of the following: Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at \"Willmott, Matsuura, and Collaborators' Global Climate Resource Pages\" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for \"Available Climate Data,\" register or sign in, and follow the link for \"South American Climate Data.\" Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf. ",
         "license": "proprietary"
     },
-    {
-        "id": "wind-topo_model_0.1.0",
-        "title": "Wind-Topo_model",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2022-01-01",
-        "end_date": "2022-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817956-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817956-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/wind-topo_model_0.1.0",
-        "description": "Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain.    It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input.    This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM.    Accompanies the publication \"Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning\" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265  Please cite this publication if you use Wind-Topo or derive new models from it.  The code can be used under the GNU Affero General Public License (AGPL).",
-        "license": "proprietary"
-    },
     {
         "id": "wind_dem_1",
         "title": "Digital Elevation Model of the Windmill Islands",
@@ -249404,45 +239433,6 @@
         "description": "The Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS dataset consists of condensed water contents, water vapor measurements, and isotope ratios in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in ASCII format from January 18, 2020, through February 28, 2023.",
         "license": "proprietary"
     },
-    {
-        "id": "wml_bilderstudie_1.0",
-        "title": "Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818010-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818010-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIiwidW1tIjoiW1wicmUtYW5hbHl6ZWQgcmVnaW9uYWwgYXZhbGFuY2hlIGRhbmdlciBsZXZlbHMgaW4gc3dpdHplcmxhbmRcIixcIkVOVklEQVRcIixcInJlLWFuYWx5c2VkLXJlZ2lvbmFsLWF2YWxhbmNoZS1kYW5nZXItbGV2ZWxzLWluLXN3aXR6ZXJsYW5kXCIsXCIxLjBcIiwzMjI2MDgyNzAzLDJdIn0%3D/wml_bilderstudie_1.0",
-        "description": "This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the \"WaMos meets LFI\" (WML) project.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wmlganzeschweiz_1.0",
-        "title": "WaMos meets LFI, ganze Schweiz",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2021-01-01",
-        "end_date": "2021-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818071-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818071-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1widmVydGVicmF0ZSBhbmQgcGxhbnQgdGF4YSByZWNvdmVyZWQgZnJvbSAxMCBjYXRjaG1lbnRzIGluIHZhdWQgdXNpbmcgYW4gZWRuYS1tZXRhYmFyY29kaW5nIGFwcHJvYWNoXCIsXCJFTlZJREFUXCIsXCJjYXRjaG1lbnQtYmlvZGl2ZXJzaXR5LXZhdWQtZWRuYVwiLFwiMS4wXCIsMzIyNjA4MTc5OSwyXSIsInVtbSI6IltcInZlcnRlYnJhdGUgYW5kIHBsYW50IHRheGEgcmVjb3ZlcmVkIGZyb20gMTAgY2F0Y2htZW50cyBpbiB2YXVkIHVzaW5nIGFuIGVkbmEtbWV0YWJhcmNvZGluZyBhcHByb2FjaFwiLFwiRU5WSURBVFwiLFwiY2F0Y2htZW50LWJpb2RpdmVyc2l0eS12YXVkLWVkbmFcIixcIjEuMFwiLDMyMjYwODE3OTksMl0ifQ%3D%3D/wmlganzeschweiz_1.0",
-        "description": "The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wood-mobilization-survey_1.0",
-        "title": "Wood Mobilization Survey",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818147-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818147-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/wood-mobilization-survey_1.0",
-        "description": "Understanding the market behavior of forest owners and managers is important to identify effective and efficient policy instruments that enhance wood provisioning. We conducted a choice experiment (CE) at two study sites in south-eastern Germany (Upper Bavaria and Lower Franconia) and two in north-eastern Switzerland (Grisons and Aargau) to elicit foresters\u2019 preferences for different supply channels, contract lengths, wood prices and duration of business relations.   CE belong to the stated preference methods to analyze individual decision making. Respondents had to choose among three options based on different attribute levels in 12 consecutive choice sets.   Our study site comparison identified regional differences and particularities, which should be taken into account when promoting wood mobilization. The success of policy instruments, such as the promotion of bundling organizations and long-term contracts, can vary depending on the specific structural and institutional conditions, like existing marketing channels, as well as on behavioral aspects of the particular public and private decision makers.",
-        "license": "proprietary"
-    },
     {
         "id": "woody_biomass_657_1",
         "title": "Woody Biomass for Eastern U.S. Forests, 1983-1996",
@@ -249469,32 +239459,6 @@
         "description": "The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.",
         "license": "proprietary"
     },
-    {
-        "id": "wsl-drought-initiative-2018_1.0",
-        "title": "Litterfall and pollen data of three LWF beech plots",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2019-01-01",
-        "end_date": "2019-01-01",
-        "bbox": "6.65804, 46.58377, 9.06707, 47.22516",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818298-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818298-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wibGlua2VkIHdhdGVyIGRpc2NoYXJnZSwgYmVkbG9hZCB0cmFuc3BvcnQgYW5kIGJlZHJvY2sgZXJvc2lvbiBkYXRhIHNldCBpbiAxbWludXRlIHJlc29sdXRpb25cIixcIkVOVklEQVRcIixcImxpbmtlZC1kaXNjaGFyZ2UtYmVkbG9hZC10cmFuc3BvcnQtYW5kLWJlZHJvY2stZXJvc2lvbi1kYXRhLXNldFwiLFwiMS4wXCIsMjc4OTgxNTM2MCw3XSIsInVtbSI6IltcImxpbmtlZCB3YXRlciBkaXNjaGFyZ2UsIGJlZGxvYWQgdHJhbnNwb3J0IGFuZCBiZWRyb2NrIGVyb3Npb24gZGF0YSBzZXQgaW4gMW1pbnV0ZSByZXNvbHV0aW9uXCIsXCJFTlZJREFUXCIsXCJsaW5rZWQtZGlzY2hhcmdlLWJlZGxvYWQtdHJhbnNwb3J0LWFuZC1iZWRyb2NrLWVyb3Npb24tZGF0YS1zZXRcIixcIjEuMFwiLDI3ODk4MTUzNjAsN10ifQ%3D%3D/wsl-drought-initiative-2018_1.0",
-        "description": "This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018).    Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen).    __Datafile:__     _LWF_beech_plots_litterfall_pollen.xlsx_    1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion.    2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models.",
-        "license": "proprietary"
-    },
-    {
-        "id": "wslintern-article-envidat-supports-open-science_1.0",
-        "title": "EnviDat Supports Open Science",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2020-01-01",
-        "end_date": "2020-01-01",
-        "bbox": "8.4546488, 47.3605728, 8.4546488, 47.3605728",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818383-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789818383-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIiwidW1tIjoiW1wiZW1lcmdlbmNlIGR5bmFtaWNzIG9mIG5hdHVyYWwgZW5lbWllcyBvZiBzcHJ1Y2UgYmFyayBiZWV0bGVzXCIsXCJFTlZJREFUXCIsXCJlbWVyZ2VuY2UtZHluYW1pY3Mtb2YtbmF0dXJhbC1lbmVtaWVzXCIsXCIxLjBcIiwyNzg5ODE0OTUzLDddIn0%3D/wslintern-article-envidat-supports-open-science_1.0",
-        "description": "The article \"EnviDat Supports Open Science\" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat\u2019s support.     License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 \"No Rights Reserved\" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.",
-        "license": "proprietary"
-    },
     {
         "id": "wygisc_wolphoyo_Not provided",
         "title": "Aerial Photos for Crazy Woman and Clear Creek Watersheds",
@@ -249507,109 +239471,5 @@
         "href": "https://cmr.earthdata.nasa.gov/stac/SCIOPS/collections?cursor=eyJqc29uIjoiW1wiYWRvcHQtYS10aWRlIHBvb2xcIixcIlNDSU9QU1wiLFwiZ29tY18xNTZcIixcIm5vdCBwcm92aWRlZFwiLDEyMTQ1ODYxNTIsNV0iLCJ1bW0iOiJbXCJhZG9wdC1hLXRpZGUgcG9vbFwiLFwiU0NJT1BTXCIsXCJnb21jXzE1NlwiLFwibm90IHByb3ZpZGVkXCIsMTIxNDU4NjE1Miw1XSJ9/wygisc_wolphoyo_Not%20provided",
         "description": "The purpose of this data was to provide a base layer of aerial photos       at the watershed scale for two areas used as part of a the Wyoming       Open Land pilot area.              Digital and registered aerial photos of Crazy Woman and Clear Creek       Watersheds, Wyoming. Each photo represents approximatley one-quarter       of a U.S.G.S. Topographic map (north-east, north-west, south-each and       south-west quarters). TIFF image format.",
         "license": "proprietary"
-    },
-    {
-        "id": "yield-15_1.0",
-        "title": "Yield",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817175-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817175-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield-15_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_and_mortality-13_1.0",
-        "title": "Yield and mortality",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817288-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817288-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_and_mortality-13_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were felled, died or disappeared between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_and_mortality_star-163_1.0",
-        "title": "Yield and mortality*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817402-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817402-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_and_mortality_star-163_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were used, died or disappeared between two inventories.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_of_live_bole_wood-87_1.0",
-        "title": "Yield of live bole wood",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817550-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817550-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_of_live_bole_wood-87_1.0",
-        "description": "Volume of stemwood at least 7 cm in diameter (limit for coarse wood) without the bark and stump that were  living trees or shrubs  starting at 12 cm dbh in the pre-inventory and were cut between two inventories.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_of_merchantable_branches-112_1.0",
-        "title": "Yield of merchantable branches",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817674-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817674-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_of_merchantable_branches-112_1.0",
-        "description": "Wood volume of branches with bark at least 7 cm in diameter (limit for coarse wood) of all living trees and shrubs  starting at 12 cm dbh that were present in the pre-inventory and cut meanwhile.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_of_merchantable_timber-114_1.0",
-        "title": "Yield of merchantable timber",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817810-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817810-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_of_merchantable_timber-114_1.0",
-        "description": "Wood volume of the stem (without bark and stump) and the branches (with bark) at least 7 cm in diameter (limit for coarse wood) from trees and shrubs  starting at 12 cm dbh that were living in the pre-inventory and were cut between the two inventories.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "yield_star-161_1.0",
-        "title": "Yield*",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817861-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817861-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/yield_star-161_1.0",
-        "description": "Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh cut between two inventories.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
-    },
-    {
-        "id": "young_forest_with_browsing_damage-193_1.0",
-        "title": "Young forest with browsing damage",
-        "catalog": "ENVIDAT STAC Catalog",
-        "state_date": "2018-01-01",
-        "end_date": "2018-01-01",
-        "bbox": "5.95587, 45.81802, 10.49203, 47.80838",
-        "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817885-ENVIDAT.umm_json",
-        "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2789817885-ENVIDAT.html",
-        "href": "https://cmr.earthdata.nasa.gov/stac/ENVIDAT/collections?cursor=eyJqc29uIjoiW1wid2ZqX21vZDogbWV0ZW9yb2xvZ2ljYWwgYW5kIHNub3dwYWNrIG1lYXN1cmVtZW50cyBmcm9tIHdlaXNzZmx1aGpvY2gsIGRhdm9zLCBzd2l0emVybGFuZFwiLFwiRU5WSURBVFwiLFwiMTAtMTY5MDQtMVwiLFwiN1wiLDI3ODk4MTQ1NDEsN10iLCJ1bW0iOiJbXCJ3ZmpfbW9kOiBtZXRlb3JvbG9naWNhbCBhbmQgc25vd3BhY2sgbWVhc3VyZW1lbnRzIGZyb20gd2Vpc3NmbHVoam9jaCwgZGF2b3MsIHN3aXR6ZXJsYW5kXCIsXCJFTlZJREFUXCIsXCIxMC0xNjkwNC0xXCIsXCI3XCIsMjc4OTgxNDU0MSw3XSJ9/young_forest_with_browsing_damage-193_1.0",
-        "description": "Number of regeneration trees where browsing of the shoots from the previous year was recorded in NFI\u2019s regeneration survey.    __Citation:__   > _Abegg, M.; Br\u00e4ndli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; R\u00f6sler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_",
-        "license": "proprietary"
     }
 ]
\ No newline at end of file
diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv
index a2a7176236..918b664fae 100644
--- a/nasa_cmr_catalog.tsv
+++ b/nasa_cmr_catalog.tsv
@@ -13,26 +13,6 @@ id	title	catalog	state_date	end_date	bbox	url	description	license
 0d2260ad4e2c42b6b14fe5b3308f5eaa_NA	ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01	FEDEO STAC Catalog	1996-03-31	2011-06-29	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143081-FEDEO.umm_json	This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI).  The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011.	proprietary
 0e289294f2c141bca545cd9d7fcb62d0_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim Glacier for 2015-2017 from Sentinel-1 data, v1.1	FEDEO STAC Catalog	2015-06-09	2017-03-21	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143491-FEDEO.umm_json	This dataset contains a time series of ice velocities for the Helheim Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between between June 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.	proprietary
 0f4324af-fa0a-4aaf-9b97-89a4f3325ce1_NA	DESIS - Hyperspectral Images - Global	FEDEO STAC Catalog	2018-08-30		-180, -52, 180, 52	https://cmr.earthdata.nasa.gov/search/concepts/C2207458058-FEDEO.umm_json	The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-13614/	proprietary
-10-16904-10_1.0	DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.92665, 46.71291, 9.92665, 46.71291	https://cmr.earthdata.nasa.gov/search/concepts/C2789814554-ENVIDAT.umm_json	This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.      The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.    Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g.  the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated.  The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.	proprietary
-10-16904-19_1.0	DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.8523577, 46.7001529, 9.9359287, 46.7393031	https://cmr.earthdata.nasa.gov/search/concepts/C2789814574-ENVIDAT.umm_json	"The data presented here corresponds to the publication ""A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation"" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland)  and its influence on snow accumulation. The dataset contains:    * Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.  * ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).  * LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).  * meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.  * TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.    For more details about the simulation and observation data, see Gerber et al., 2017.    __Publication__: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres."	proprietary
-10-16904-1_7	WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.809568, 46.829598, 9.809568, 46.829598	https://cmr.earthdata.nasa.gov/search/concepts/C2789814541-ENVIDAT.umm_json	Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.     __Publication__  Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.	proprietary
-10-16904-21_1.0	Wind crust formation: SnowMicroPen data	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.86752, 46.80798, 9.86752, 46.80798	https://cmr.earthdata.nasa.gov/search/concepts/C2789814603-ENVIDAT.umm_json	This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.     The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.    Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.    This dataset is the basis of the following publication:    Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53	proprietary
-10-16904-22_1.0	Wind crust formation: Microsoft Kinect data	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.86752, 46.80798, 9.86752, 46.80798	https://cmr.earthdata.nasa.gov/search/concepts/C2789814617-ENVIDAT.umm_json	This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.    The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.    Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.    This dataset is the basis of the following publication:    Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004	proprietary
-10-16904-23_1.0	Precipitation Scaling Data Set (Vögeli et al., Frontiers)	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.7098541, 46.6866604, 10.0037384, 46.8606605	https://cmr.earthdata.nasa.gov/search/concepts/C2789814633-ENVIDAT.umm_json	"Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.    This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.     The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.    Please cite:    _Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._    Dataset is provided as a single zip file.   The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.   The archive also contains the file: ""ReadMeMetadataDataSetPrecipitationScaling"" which explains the data structure."	proprietary
-10-16904-2_1	Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.809568, 46.829598, 9.809568, 46.829598	https://cmr.earthdata.nasa.gov/search/concepts/C2789814584-ENVIDAT.umm_json	Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris].	proprietary
-10-16904-3_1	Forest Access Roads 2013	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814652-ENVIDAT.umm_json	In 2013–2014, a survey was conducted in Switzerland to update the Forest Access Roads geo-dataset within the framework of the Swiss National Forest Inventory (NFI). The resulting nationwide dataset contains valuable information on truck-accessible forest roads that can be used to transport wood. The survey involved interviewing staff from the approximately 800 local forest services in Switzerland and recording the data first on paper maps and then in digitized form. The data in the NFI on the forest roads could thus be updated and additional information regarding their trafficability for specific categories of truck included. The information has now been attached to the geometries of the Roads and Tracks of the swissTLM3D (release 2012) of the Federal Office of Topography swisstopo. The resulting data are suitable for statistical analyses and modeling, but further (labour-intensive) validation work would be necessary if they are to be used as a basis for applications requiring more spatial accuracy, such as navigation systems. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available for third parties for non-commercial use provided they have purchased a TLM license.     __Related Publication__: [doi: 10.3188/szf.2016.0136](http://dx.doi.org/10.3188/szf.2016.0136)	proprietary
-10-16904-4_1	TRAMM project Ruedlingen experimental landslide dataset, Switzerland	ENVIDAT STAC Catalog	2015-01-01	2015-01-01	8.56659, 47.56685, 8.56659, 47.56685	https://cmr.earthdata.nasa.gov/search/concepts/C2789814663-ENVIDAT.umm_json	A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38° to 43° with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2).     __Publications__    1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992–8004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560).    2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)	proprietary
-10-16904-5_1	TRAMM project - experimental hydrological and hydrogeological dataset of a landslide prone hillslope. Rufiberg, Switzerland	ENVIDAT STAC Catalog	2015-01-01	2015-01-01	8.5544251, 47.0889606, 8.5544251, 47.0889606	https://cmr.earthdata.nasa.gov/search/concepts/C2789814727-ENVIDAT.umm_json	Rufiberg is a pre-alpine meadow site in Switzerland where shallow landslides have been observed after past intense rain storms. In order to assess the triggering mechanisms of these landslides, a comprehensive investigation was conducted within the project TRAMM from Nov 2009 to Oct 2012. It included meteorological observations, soil moisture measurements, bedrock groundwater measurements. The Rufiberg is located at the NW side of the Gnipen to the north of the village Arth-Goldau in the Canton of Schwyz. In the summer months, the site is used for pasturing. Usually, from December to March a snow cover is present at the Rufiberg. The site is at an altitude between 1080 – 1180 m asl, is ENE oriented, and has an average slope of 30 -35°. The Subalpine Molasse in the region is inclined with 30 - 35° to SE. In the area of the field site, beds of conglomerate with several m of thickness alter with beds of sandstone and marlstone. A ca. 2 – 5 m thick eluvium/colluvium layer composed of silty and sandy clay covers the bedrock. This site has been chosen because on one hand, during heavy rainfall events, e.g. autumn 2005, numerous landslides occur in the region of the Gnipen and the Rufiberg. On the other hand, the Rufiberg is very appropriate for experiments due its location away from infrastructures and due to its accessibility. The goal of the investigation was to understand the hydrology and hydrogeology of the slope with regard to shallow landslides.     More information: Brönnimann, C., Stähli, M., Schneider, P., Seward, L. and Springman, S.M. 2013. Bedrock exfiltration as a triggering mechanism for shallow landslides. Water Resources Research, 49 (9): 5155–5167. DOI: 10.1002/wrcr.20386.	proprietary
-10-16904-6_1	Wind tunnel measurement data of drifting snow and turbulent wind fluctuations	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.84726, 46.81204, 9.84726, 46.81204	https://cmr.earthdata.nasa.gov/search/concepts/C2789814796-ENVIDAT.umm_json	The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.     __Publication__ http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract	proprietary
-10-16904-7_1	More than one century of hydrological monitoring in two small catchments with different forest coverage, Sperbelgraben and Rappengraben (Switzerland)	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	7.84105, 47.01343, 7.8903, 47.03105	https://cmr.earthdata.nasa.gov/search/concepts/C2789814861-ENVIDAT.umm_json	Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land-use and climate change. Here the longest continuous runoff records (1903 - 2015) from small catchments (less than 1 km2) in Switzerland (and possibly worldwide) are provided as a data set. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized in Stähli et al., Environ Monit Assess (2011). The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty (also discussed in Stähli et al., 2011). This treasure trove of data can be used in various ways, eg. for analysis of the generalized extreme value distributions of the two catchments, of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic models.	proprietary
-10-16904-8_1	Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	180, -89.7, -180, -37	https://cmr.earthdata.nasa.gov/search/concepts/C2789814950-ENVIDAT.umm_json	This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.    _Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._    __Related publication__: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)    Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.	proprietary
-10-16904-9_1	High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	114, -66, 122, -63	https://cmr.earthdata.nasa.gov/search/concepts/C2789814985-ENVIDAT.umm_json	This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.    ###How to cite:  Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).	proprietary
-10-16904-envidat-24_1.0	Influence of slope-scale snowmelt on catchment response simulated with the Alpine3D model	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.8197174, 46.672292, 9.9893188, 46.8012345	https://cmr.earthdata.nasa.gov/search/concepts/C2789815030-ENVIDAT.umm_json	# Abstract  Snow and hydrological modeling in alpine environments remains a challenge because of the complexity of the processes complexity affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high-alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014-2015. Based on recent advances in Alpine3D, we examine how modeled snow distributions, and modeled liquid water transport within the snowpack influence runoff dynamics. By combining these results with multi-scale field data (snow lysimeter data, distributed snow depths and streamflow), we demonstrate the added value of a more realistic representation of snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the snowpack mass balance errors are corrected (R2 = 0.95 vs. R2 = 0.61). At the sub-basin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originated in the shallower areas while an extended melting period (by more than a month) is caused by slower snowmelt from deeper areas. This result is a marked improvement over results obtained using a less heterogeneous snow distribution (i.e., traditional precipitation interpolation method). Catchment hydrological response is also improved by the more realistic representation of snowpack heterogeneity (Nash coefficient of 0.85 vs. 0.74), even though the calibration process smoothens out the differences.   The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at the sub-basin and basin scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well-instrumented area and present a model validation from multi-scale experimental datasets.	proprietary
-10-16904-envidat-25_1	DISCHMEX - High-resolution daily snow ablation rates in an Alpine environment	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.92665, 46.71291, 9.92665, 46.71291	https://cmr.earthdata.nasa.gov/search/concepts/C2789814543-ENVIDAT.umm_json	We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007°. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.	proprietary
-10-16904-envidat-27_1.0	Calibration data for empirical mortality models of 18 European tree species	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	5.8447266, 45.7521934, 11.7773438, 53.917281	https://cmr.earthdata.nasa.gov/search/concepts/C2789814553-ENVIDAT.umm_json	The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.     ## Inventory data  Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.     ## Data selection  We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).  We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..     ## Predictors of tree mortality  We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.     ## Further information  For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.	proprietary
-10-16904-envidat-28_1.0	Snowfarming data set Davos and Martell 2015	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.868, 46.517, 10.727, 46.808	https://cmr.earthdata.nasa.gov/search/concepts/C2789814571-ENVIDAT.umm_json	Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.     The data set contains for each site:    * 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)    * 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)    Input files for SNOWPACK model:    * .sno: snow profile at the end of winter    * .smet: meteorological data measured by weather stations in the area    For more details see  Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.	proprietary
-10-16904-envidat-29_1.0	Automatic detection of avalanches	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.78759, 46.80616, 9.78759, 46.80616	https://cmr.earthdata.nasa.gov/search/concepts/C2789814583-ENVIDAT.umm_json	This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.	proprietary
-10-16904-envidat-30_1.0	Expedition to Princess Elisabeth Antarctica Station, 2016/2017	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	23.35, -71.95, 23.35, -71.95	https://cmr.earthdata.nasa.gov/search/concepts/C2789814600-ENVIDAT.umm_json	This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.    Please refer to the README for more information about the data.    This dataset is the basis of the following publication:    Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.	proprietary
 10.25921/0haq-t221_Not Applicable	Chlorofluorocarbons, nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22 (NCEI Accession 0186104)	NOAA_NCEI STAC Catalog	2009-11-26	2009-12-22	-31.03, 3.76, -15, 17.44	https://cmr.earthdata.nasa.gov/search/concepts/C2089379193-NOAA_NCEI.umm_json	This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, nutrients and other measurements. R/V Meteor Cruise No. 80/2 was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.	proprietary
 10.25921/16y6-9e29_Not Applicable	Chlorofluorocarbon (CFC-12), sulfur hexafluoride (SF6), water temperature, salinity, nutrients, dissolved oxygen and other measurements collected from discrete samples and profile observations during the R/V Meteor cruise M135 (EXPOCODE 06MT20170302) in the South Pacific Ocean from 2017-03-02 to 2017-04-07 (NCEI Accession 0232257)	NOAA_NCEI STAC Catalog	2017-03-02	2017-04-07	-86, -31.03, -70, -10.67	https://cmr.earthdata.nasa.gov/search/concepts/C2089380481-NOAA_NCEI.umm_json	This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M135 (EXPOCODE 06MT20170302) in the South Pacific Ocean from 2017-03-02 to 2017-04-07. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Pacific Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface.	proprietary
 10.25921/3bmf-xc16_Not Applicable	Carbon dioxide, hydrographic, and chemical discrete profile data obtained during the R/V N.B. Palmer cruise in the South Pacific Ocean on GO-SHIP/CLIVAR/SOCCOM Repeat Hydrography Sections P06W (EXPOCODE 320620170703) and P06E (EXPOCODE 320620170820) from 2017-07-03 to 2017-09-30 (NCEI Accession 0175744)	NOAA_NCEI STAC Catalog	2017-07-03	2017-09-30	152.9, -32.5051, -71.585, -28.9597	https://cmr.earthdata.nasa.gov/search/concepts/C2089380674-NOAA_NCEI.umm_json	This NCEI Accession includes discrete bottle measurements of dissolved inorganic carbon (DIC), total alkalinity, pH on total scale, partial pressure of CO2, dissolved organic carbon (DOC), CFCs, temperature, salinity, oxygen, nutrients, and other variables measured during R/V N.B. Palmer cruise in the South Pacific Ocean on GO-SHIP/CLIVAR/SOCCOM Repeat Hydrography Sections P06W (EXPOCODE 320620170703) and P06E (EXPOCODE 320620170820) from 2017-07-03 to 2017-09-30. The Pacific Ocean P06 repeat hydrographic line was reoccupied for the US Global Ocean Carbon and Repeat Hydrography Program. Reoccupation of the P06E transect occurred on the RVIB Nathaniel B Palmer from August 20, 2017 to September 30, 2017. The survey of P06 2017 consisted of CTDO, rosette, LADCP, chipod, water samples and underway measurements. The ship departed from the port of Papeete on the island of Tahiti, French Polynesia and completed the cruise in the port of Valparaiso, Chile.	proprietary
@@ -353,7 +333,6 @@ id	title	catalog	state_date	end_date	bbox	url	description	license
 201617020_1	Aurora Australis Voyage 2 2016/17 Track and Underway Data	AU_AADC STAC Catalog	2016-12-08	2017-01-21	110, -66.5, 147, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1380161179-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2016/17 season.  Purpose of voyage: Casey Resupply, recover and deploy whale mooring, SOTS mooring, krill trawl and SR3 transect.  Leader: Mr. Andy Cianchi Deputy Leader: Mr. Mike Woolridge  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
 201617030_1	Aurora Australis Voyage 3 2016/17 Track and Underway Data	AU_AADC STAC Catalog	2017-01-25	2017-03-11	62, -67.5, 147, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1380161244-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2016/17 season.  Purpose of voyage: Mawson Resupply, Davis Summer Retrieval, recover and deploy whale mooring.  Leader: Ms. Leanne Millhouse Deputy Leader: Mr. Simon Langdon  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
 201617040_1	Aurora Australis Voyage 4 2016/17 Track and Underway Data	AU_AADC STAC Catalog	2017-03-05	2017-03-19	147, -54.5, 160, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1397605597-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2016/17 season.  Purpose of voyage: Macquarie Island over water resupply and refuel, personnel deployment/retrieval and approved project support.  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
-2016gl071822_1.0	Energy- and momentum-conserving model of splash entrainment in sand and snow saltation	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	6.56689, 46.51959, 6.56689, 46.51959	https://cmr.earthdata.nasa.gov/search/concepts/C2789814614-ENVIDAT.umm_json	The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).    ## Manuscript Abstract:  Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.	proprietary
 201718010_1	Aurora Australis Voyage 1 2017/18 Track and Underway Data	AU_AADC STAC Catalog	2017-10-29	2017-12-03	76, -67.5, 147, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1517284074-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 1 of the Aurora Australis Voyage in the 2017/18 season.  Purpose of voyage: Davis Resupply - Davis over ice resupply, refuel and personnel deployment/retrieval. Deploy helicopters to Davis station.  Leader: Dr. Doug Thost Deputy Leader: Mr. Andrew Cawthorn  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
 201718020_1	Aurora Australis Voyage 2 2017/18 Track and Underway Data	AU_AADC STAC Catalog	2017-12-13	2018-01-11	110, -66.5, 147, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1517284085-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 2 of the Aurora Australis Voyage in the 2017/18 season.  Purpose of voyage: Casey Resupply, recover and deploy whale mooring, krill trawl.  Leader: Mr. James Moloney Deputy Leader: Mr. Dave Pryce  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
 201718030_1	Aurora Australis Voyage 3 2017/18 Track and Underway Data	AU_AADC STAC Catalog	2018-01-16	2018-03-06	62, -67.5, 147, -42.4	https://cmr.earthdata.nasa.gov/search/concepts/C1517284088-AU_AADC.umm_json	"On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds.  These data are known as the underway datasets.  The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on.  For the full list of available data types, see the website.      These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link).  Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL).  This dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2017/18 season.  Purpose of voyage: Mawson over water resupply and refuel, deploy/retrieve personnel, recover and deploy whale mooring.  Leader: Mr. Mark Skinner Deputy Leader: Dr. Fred Olivier  Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)."	proprietary
@@ -540,7 +519,6 @@ id	title	catalog	state_date	end_date	bbox	url	description	license
 3b3fd2daf3d34c1bb4a09efeaf3b8ea9_NA	ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2019), version 1.0	FEDEO STAC Catalog	2000-02-24	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142546-FEDEO.umm_json	This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2019. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.	proprietary
 3bfe0c2d51544f72837a99306a74e359_NA	ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1	FEDEO STAC Catalog	1978-11-01	2020-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143225-FEDEO.umm_json	"An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su,  C. -H., Gruber, A. and Dorigo, W., ""Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,"" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896."	proprietary
 3c324bb4ee394d0d876fe2e1db217378_NA	ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.0	FEDEO STAC Catalog	1992-09-26	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142668-FEDEO.umm_json	"This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and   functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:•	Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.•	Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.•	Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. •	Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. •	Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents."	proprietary
-3d_snow_models_4.0	3D_Snow_Models	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8471832, 46.8146287, 9.8471832, 46.8146287	https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json	The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).	proprietary
 3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA	METOP GOME-2 - Formaldehyde (HCHO) - Global	FEDEO STAC Catalog	2007-01-23		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2207457991-FEDEO.umm_json	"The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.			The operational HCHO total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.			For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/"	proprietary
 3fe263d2-99ed-4751-b937-d26a31ab0606_NA	AVHRR - Vegetation Index (NDVI) - Europe	FEDEO STAC Catalog	1994-07-01		-24, 28, 57, 78	https://cmr.earthdata.nasa.gov/search/concepts/C2207458021-FEDEO.umm_json	"Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the ""Normalized Difference Vegetation Index"" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the ""greenness"" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/"	proprietary
 4003949a-cb4b-41b7-9710-915269990bcd_NA	IRS-1D - Panchromatic Images (PAN) - Europe	FEDEO STAC Catalog	1999-01-01	2005-01-27	-25, 30, 45, 80	https://cmr.earthdata.nasa.gov/search/concepts/C2207458009-FEDEO.umm_json	Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale.	proprietary
@@ -1473,8 +1451,8 @@ AERDT_L2_VIIRS_NOAA20_NRT_2	VIIRS/NOAA-20 Dark Target Aerosol L2 6-Min Swath (v2
 AERDT_L2_VIIRS_SNPP_2	VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km V2	LAADS STAC Catalog	2012-03-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2771506686-LAADS.umm_json	The VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally.  The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) incarnation of the dark target (DT) aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission&#8217;s MODIS instruments.  Two separate and distinct DT algorithms exist.  One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).    This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1).    For more information consult LAADS product description page at:    https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_SNPP    Or, Dark Target aerosol team Page at:   https://darktarget.gsfc.nasa.gov/	proprietary
 AERDT_L2_VIIRS_SNPP_NRT_1.1	VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath	ASIPS STAC Catalog	2020-06-09		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1976333380-ASIPS.umm_json	The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).	proprietary
 AERDT_L2_VIIRS_SNPP_NRT_2	VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath (v2.0)	ASIPS STAC Catalog	2023-11-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2812412751-ASIPS.umm_json	The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible).    This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1).	proprietary
-AERIALDIGI_Not provided	Aircraft Scanners	USGS_LTA STAC Catalog	1987-10-06		-180, 24, -60, 72	https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json	The National Aeronautics and Space Administration (NASA) Aircraft        Scanners data set contains digital imagery acquired from several        multispectral scanners, including Daedalus thematic mapper simulator       scanners and  the thermal infrared multispectral scanner.  Data are       collected from  selected areas over the conterminous United States,       Alaska, and Hawaii by  NASA ER-2 and NASA C-130B aircraft, operating       from the NASA Ames Research  Center in Moffett Field, California, and by       NASA Learjet aircraft, operating  from Stennis Space Center in Bay St.       Louis, Mississippi.  Limited  international acquisitions also are       available.              In cooperation with the Jet Propulsion Laboratory and Daedalus        Enterprises,Inc., NASA developed several multispectral sensors.  The       data  acquired from these sensors supports NASA's Airborne Science and       Applications  Program and have been identified as precursors to the       instruments scheduled to  fly on Earth Observing System platforms.              THEMATIC MAPPER SIMULATOR              The Thematic Mapper Simulator (TMS) sensor is a line scanning device          designed for a variety of Earth science applications.  Flown aboard              NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field  of              View of 1.25 milliradians with a ground resolution of 81 feet (25  meters)              at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per  second              with 716 pixels per scan line.  Swath width is 8.3 nautical miles              (15.4 kilometers) at 65,000 feet while the scanner's Field of View  is              42.5 degrees.                NS-001 MULTISPECTRAL SCANNER               The NS-001multispectral scanner is a line scanning device designed  to              simulate Landsat thematic mapper (TM) sensor performance, including  a              near infrared/short-wave infrared band used in applications similar  to those         of the TM sensor (e.g., Earth resources mapping, vegetation/land  cover          mapping, geologic studies).  Flown aboard NASA C-130B aircraft, the  NS-001       sensor has a nominal Instantaneous Field of View of 2.5 milliradians     with a       ground resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a       variable scan rate (10 to 100 scans per second) with 699  pixels per scan line,       but the available motor drive supply restricts the  maximum stable scan speed       to approximately 85 revolutions per second. A scan rate  of 100 revolutions per       second is possible, but not probable, for short  scan lines; therefore, a       combination of factors, including aircraft  flight requirements and maximum       scan speed, prevent scanner operation below 1,500 feet.  Swath width is 3.9       nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or       field of regard for the sensor is 100 degrees, plus or minus 15 degrees for       roll compensation.       THERMAL INFRARED MULTISPECTRAL SCANNER       The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line  scanning         device originally designed for geologic applications.  Flown aboard NASA       C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor  has a              nominal Instantaneous Field of View of 2.5 milliradians with a  ground              resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a              selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698  pixels          per scan line.  Swath width is 2.6 nautical miles (4.8 kilometers)  at            10,000 feet while the scanner's Field of View is 76.56 degrees.	proprietary
 AERIALDIGI_Not provided	Aircraft Scanners - AERIALDIGI	CEOS_EXTRA STAC Catalog	1987-10-06		-180, 24, -60, 72	https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json	The National Aeronautics and Space Administration (NASA) Aircraft        Scanners data set contains digital imagery acquired from several        multispectral scanners, including Daedalus thematic mapper simulator       scanners and  the thermal infrared multispectral scanner.  Data are       collected from  selected areas over the conterminous United States,       Alaska, and Hawaii by  NASA ER-2 and NASA C-130B aircraft, operating       from the NASA Ames Research  Center in Moffett Field, California, and by       NASA Learjet aircraft, operating  from Stennis Space Center in Bay St.       Louis, Mississippi.  Limited  international acquisitions also are       available.              In cooperation with the Jet Propulsion Laboratory and Daedalus        Enterprises,Inc., NASA developed several multispectral sensors.  The       data  acquired from these sensors supports NASA's Airborne Science and       Applications  Program and have been identified as precursors to the       instruments scheduled to  fly on Earth Observing System platforms.              THEMATIC MAPPER SIMULATOR              The Thematic Mapper Simulator (TMS) sensor is a line scanning device          designed for a variety of Earth science applications.  Flown aboard              NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field  of              View of 1.25 milliradians with a ground resolution of 81 feet (25  meters)              at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per  second              with 716 pixels per scan line.  Swath width is 8.3 nautical miles              (15.4 kilometers) at 65,000 feet while the scanner's Field of View  is              42.5 degrees.                NS-001 MULTISPECTRAL SCANNER               The NS-001multispectral scanner is a line scanning device designed  to              simulate Landsat thematic mapper (TM) sensor performance, including  a              near infrared/short-wave infrared band used in applications similar  to those         of the TM sensor (e.g., Earth resources mapping, vegetation/land  cover          mapping, geologic studies).  Flown aboard NASA C-130B aircraft, the  NS-001       sensor has a nominal Instantaneous Field of View of 2.5 milliradians     with a       ground resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a       variable scan rate (10 to 100 scans per second) with 699  pixels per scan line,       but the available motor drive supply restricts the  maximum stable scan speed       to approximately 85 revolutions per second. A scan rate  of 100 revolutions per       second is possible, but not probable, for short  scan lines; therefore, a       combination of factors, including aircraft  flight requirements and maximum       scan speed, prevent scanner operation below 1,500 feet.  Swath width is 3.9       nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or       field of regard for the sensor is 100 degrees, plus or minus 15 degrees for       roll compensation.       THERMAL INFRARED MULTISPECTRAL SCANNER       The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line  scanning         device originally designed for geologic applications.  Flown aboard NASA       C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor  has a              nominal Instantaneous Field of View of 2.5 milliradians with a  ground              resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a              selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698  pixels          per scan line.  Swath width is 2.6 nautical miles (4.8 kilometers)  at            10,000 feet while the scanner's Field of View is 76.56 degrees.	proprietary
+AERIALDIGI_Not provided	Aircraft Scanners	USGS_LTA STAC Catalog	1987-10-06		-180, 24, -60, 72	https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json	The National Aeronautics and Space Administration (NASA) Aircraft        Scanners data set contains digital imagery acquired from several        multispectral scanners, including Daedalus thematic mapper simulator       scanners and  the thermal infrared multispectral scanner.  Data are       collected from  selected areas over the conterminous United States,       Alaska, and Hawaii by  NASA ER-2 and NASA C-130B aircraft, operating       from the NASA Ames Research  Center in Moffett Field, California, and by       NASA Learjet aircraft, operating  from Stennis Space Center in Bay St.       Louis, Mississippi.  Limited  international acquisitions also are       available.              In cooperation with the Jet Propulsion Laboratory and Daedalus        Enterprises,Inc., NASA developed several multispectral sensors.  The       data  acquired from these sensors supports NASA's Airborne Science and       Applications  Program and have been identified as precursors to the       instruments scheduled to  fly on Earth Observing System platforms.              THEMATIC MAPPER SIMULATOR              The Thematic Mapper Simulator (TMS) sensor is a line scanning device          designed for a variety of Earth science applications.  Flown aboard              NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field  of              View of 1.25 milliradians with a ground resolution of 81 feet (25  meters)              at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per  second              with 716 pixels per scan line.  Swath width is 8.3 nautical miles              (15.4 kilometers) at 65,000 feet while the scanner's Field of View  is              42.5 degrees.                NS-001 MULTISPECTRAL SCANNER               The NS-001multispectral scanner is a line scanning device designed  to              simulate Landsat thematic mapper (TM) sensor performance, including  a              near infrared/short-wave infrared band used in applications similar  to those         of the TM sensor (e.g., Earth resources mapping, vegetation/land  cover          mapping, geologic studies).  Flown aboard NASA C-130B aircraft, the  NS-001       sensor has a nominal Instantaneous Field of View of 2.5 milliradians     with a       ground resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a       variable scan rate (10 to 100 scans per second) with 699  pixels per scan line,       but the available motor drive supply restricts the  maximum stable scan speed       to approximately 85 revolutions per second. A scan rate  of 100 revolutions per       second is possible, but not probable, for short  scan lines; therefore, a       combination of factors, including aircraft  flight requirements and maximum       scan speed, prevent scanner operation below 1,500 feet.  Swath width is 3.9       nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or       field of regard for the sensor is 100 degrees, plus or minus 15 degrees for       roll compensation.       THERMAL INFRARED MULTISPECTRAL SCANNER       The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line  scanning         device originally designed for geologic applications.  Flown aboard NASA       C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor  has a              nominal Instantaneous Field of View of 2.5 milliradians with a  ground              resolution of 25 feet (7.6 meters) at 10,000 feet.  The sensor has a              selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698  pixels          per scan line.  Swath width is 2.6 nautical miles (4.8 kilometers)  at            10,000 feet while the scanner's Field of View is 76.56 degrees.	proprietary
 AERONET_aerosol_706_1	SAFARI 2000 AERONET Ground-based Aerosol Data, Dry Season 2000	ORNL_CLOUD STAC Catalog	1999-01-01	2001-12-31	28.03, -26.19, 28.03, -26.19	https://cmr.earthdata.nasa.gov/search/concepts/C2788355135-ORNL_CLOUD.umm_json	AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign. AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes.	proprietary
 AEROSE_0	Saharan Dust AERosols and Ocean Science Expeditions	OB_DAAC STAC Catalog	2004-03-02	2017-04-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2108358203-OB_DAAC.umm_json	AEROSE is an internationally recognized series of trans-Atlantic field campaigns conducted onboard the NOAA Ship Ronald H. Brown designed to explore African air mass outflows and their impacts on climate, weather, and environmental health.	proprietary
 AE_5DSno_2	AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids V002	NSIDC_ECS STAC Catalog	2002-06-20	2011-10-03	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C179014698-NSIDC_ECS.umm_json	These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids).	proprietary
@@ -2426,49 +2404,49 @@ ATL02_006	ATLAS/ICESat-2 L1B Converted Telemetry Data V006	NSIDC_CPRD STAC Catal
 ATL03_006	ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json	This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.	proprietary
 ATL03_006	ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json	This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.	proprietary
 ATL03_ANC_MASKS_1	ATLAS/ICESat-2 ATL03 Ancillary Masks, Version 1	NSIDCV0 STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2278879612-NSIDCV0.umm_json	This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references.	proprietary
-ATL04_006	ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json	ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL04_006	ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json	ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
+ATL04_006	ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json	ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL06_006	ATLAS/ICESat-2 L3A Land Ice Height V006	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.umm_json	This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL06_006	ATLAS/ICESat-2 L3A Land Ice Height V006	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json	This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL07QL_006	ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006	NSIDC_ECS STAC Catalog	2024-08-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json	ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL07_006	ATLAS/ICESat-2 L3A Sea Ice Height V006	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json	The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL07_006	ATLAS/ICESat-2 L3A Sea Ice Height V006	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json	The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL08QL_006	ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006	NSIDC_ECS STAC Catalog	2024-08-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json	ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed.  ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
-ATL08_006	ATLAS/ICESat-2 L3A Land and Vegetation Height V006	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json	This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL08_006	ATLAS/ICESat-2 L3A Land and Vegetation Height V006	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json	This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
+ATL08_006	ATLAS/ICESat-2 L3A Land and Vegetation Height V006	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json	This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL09QL_006	ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006	NSIDC_ECS STAC Catalog	2024-08-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json	ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed.  ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
-ATL09_006	ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json	This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL09_006	ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json	This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
+ATL09_006	ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json	This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL10QL_006	ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006	NSIDC_ECS STAC Catalog	2024-08-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json	ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
-ATL10_006	ATLAS/ICESat-2 L3A Sea Ice Freeboard V006	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json	This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL10_006	ATLAS/ICESat-2 L3A Sea Ice Freeboard V006	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json	This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
+ATL10_006	ATLAS/ICESat-2 L3A Sea Ice Freeboard V006	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json	This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL11_006	ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006	NSIDC_ECS STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json	This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.	proprietary
 ATL11_006	ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006	NSIDC_CPRD STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json	This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.	proprietary
-ATL12_006	ATLAS/ICESat-2 L3A Ocean Surface Height V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json	This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL12_006	ATLAS/ICESat-2 L3A Ocean Surface Height V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json	This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
+ATL12_006	ATLAS/ICESat-2 L3A Ocean Surface Height V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json	This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.	proprietary
 ATL13QL_006	ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006	NSIDC_ECS STAC Catalog	2024-08-30		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json	ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).	proprietary
 ATL13_006	ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json	This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).	proprietary
 ATL13_006	ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json	This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).	proprietary
-ATL14_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003	NSIDC_CPRD STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
 ATL14_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003	NSIDC_ECS STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
-ATL14_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004	NSIDC_CPRD STAC Catalog	2019-01-01	2023-12-28	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json	This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
+ATL14_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003	NSIDC_CPRD STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
 ATL14_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004	NSIDC_ECS STAC Catalog	2019-01-01	2023-12-28	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json	This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
-ATL15_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003	NSIDC_CPRD STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
+ATL14_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004	NSIDC_CPRD STAC Catalog	2019-01-01	2023-12-28	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json	This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
 ATL15_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003	NSIDC_ECS STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
-ATL15_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004	NSIDC_CPRD STAC Catalog	2019-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json	This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
+ATL15_003	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003	NSIDC_CPRD STAC Catalog	2019-03-29		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776895930-NSIDC_CPRD.umm_json	ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR).   ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change.	proprietary
 ATL15_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004	NSIDC_ECS STAC Catalog	2019-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3159684532-NSIDC_ECS.umm_json	This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
+ATL15_004	ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004	NSIDC_CPRD STAC Catalog	2019-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json	This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11).	proprietary
 ATL16_005	ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json	This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.	proprietary
 ATL16_005	ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json	This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.	proprietary
 ATL17_005	ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json	This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.	proprietary
 ATL17_005	ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005	NSIDC_ECS STAC Catalog	2018-10-13		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json	This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.	proprietary
 ATL19_003	ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003	NSIDC_ECS STAC Catalog	2018-10-13		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json	This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.	proprietary
 ATL19_003	ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json	This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.	proprietary
-ATL20_004	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json	ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.	proprietary
 ATL20_004	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json	ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.	proprietary
-ATL21_003	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.umm_json	ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.	proprietary
+ATL20_004	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json	ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection.	proprietary
 ATL21_003	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2753316241-NSIDC_CPRD.umm_json	ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.	proprietary
-ATL22_003	ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003	NSIDC_ECS STAC Catalog	2018-10-14		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json	ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.	proprietary
+ATL21_003	ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003	NSIDC_ECS STAC Catalog	2018-10-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.umm_json	ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids.	proprietary
 ATL22_003	ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003	NSIDC_CPRD STAC Catalog	2018-10-14		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.umm_json	ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.	proprietary
+ATL22_003	ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003	NSIDC_ECS STAC Catalog	2018-10-14		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json	ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.	proprietary
 ATL23_001	ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001	NSIDC_CPRD STAC Catalog	2018-10-13		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.umm_json	This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.	proprietary
 ATL23_001	ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001	NSIDC_ECS STAC Catalog	2018-10-13		-180, -88, 180, 88	https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.umm_json	This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.	proprietary
 ATLAS_DEALIASED_SASS_L2_1	SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (Atlas)	POCLOUD STAC Catalog	1978-07-07	1978-10-10	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2617197627-POCLOUD.umm_json	Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz.	proprietary
@@ -3994,7 +3972,7 @@ CIESIN_SEDAC_GEO-MEX_POPDBMEX_1.00	Georeferenced Population Datasets of Mexico (
 CIESIN_SEDAC_GEO-MEX_RASTERGIS_1.00	Georeferenced Population Datasets of Mexico (GEO-MEX): Raster Based GIS Coverage of Mexican Population	SEDAC STAC Catalog	1990-01-01	1990-12-31	-117, 14, -86, 33	https://cmr.earthdata.nasa.gov/search/concepts/C179001922-SEDAC.umm_json	The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).	proprietary
 CIESIN_SEDAC_GEO-MEX_URBANGIS_1.00	Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place GIS Coverage of Mexico	SEDAC STAC Catalog	1921-01-01	1990-12-31	-117, 14, -86, 33	https://cmr.earthdata.nasa.gov/search/concepts/C179001923-SEDAC.umm_json	The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is  produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.	proprietary
 CIESIN_SEDAC_GEO-MEX_URB_TSER_1.00	Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place Time-Series Population of Mexico	SEDAC STAC Catalog	1921-01-01	1990-12-31	-117, 14, -87, 33	https://cmr.earthdata.nasa.gov/search/concepts/C179001761-SEDAC.umm_json	The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).	proprietary
-CIESIN_SEDAC_GHSL_PBSMOD_1.0	Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid	SEDAC STAC Catalog	1975-01-01	2015-01-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.umm_json	The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.	proprietary
+CIESIN_SEDAC_GHSL_PBSMOD_1.00	Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid	SEDAC STAC Catalog	1975-01-01	2015-01-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2098218461-SEDAC.umm_json	The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.	proprietary
 CIESIN_SEDAC_GPWv3_CENTROIDS_3.00	Gridded Population of the World, Version 3 (GPWv3): Centroids	SEDAC STAC Catalog	1990-07-01	2015-07-01	-179.199552, -54.751503, 179.848323, 79.89196	https://cmr.earthdata.nasa.gov/search/concepts/C1428079129-SEDAC.umm_json	The Gridded Population of the World, Version 3 (GPWv3): Centroids consists of estimates of human population counts and densities for the years 1990, 1995, 2000, 2005, 2010, and 2015 by administrative Unit centroid location. The centroids are based on the 399,781 input administrative Units used in GPWv3. In addition to population counts and variables, the centroids have associated administrative Unit names and the land area of contained within the administrative Unit. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).	proprietary
 CIESIN_SEDAC_GPWv3_COASTLINES_3.00	Gridded Population of the World, Version 3 (GPWv3): Coastlines	SEDAC STAC Catalog	2000-07-01	2000-07-01	-179.199552, -54.751503, 179.848323, 79.89196	https://cmr.earthdata.nasa.gov/search/concepts/C1000000003-SEDAC.umm_json	The Gridded Population of the World, Version 3 (GPWv3): Coastlines are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GPWv3. The coastlines are designed for cartographic use with the GPWv3 population raster data sets. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).	proprietary
 CIESIN_SEDAC_GPWv3_LANDGEOG_3.00	Gridded Population of the World, Version 3 (GPWv3): Land and Geographic Unit Area Grids	SEDAC STAC Catalog	1990-07-01	2015-07-01	-179.199552, -54.751503, 179.848323, 79.89196	https://cmr.earthdata.nasa.gov/search/concepts/C1000000022-SEDAC.umm_json	The Gridded Population of the World, Version 3 (GPWv3): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived..GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).	proprietary
@@ -6157,34 +6135,34 @@ GHISACONUS_001	Global Hyperspectral Imaging Spectral-library of Agricultural cro
 GIMMS3g_NDVI_Trends_1275_1	Long-Term Arctic Growing Season NDVI Trends from GIMMS 3g, 1982-2012	ORNL_CLOUD STAC Catalog	1982-06-01	2012-08-31	-180, 20, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784897341-ORNL_CLOUD.umm_json	This data set provides normalized difference vegetation index (NDVI) data for the arctic growing season derived primarily with data from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard several NOAA satellites over the years 1982 through 2012. The NDVI data, which show vegetation activity, were averaged annually for the arctic growing season (GS; June, July and August). The products include the annual GS-NDVI values and the results of a cumulative GS-NDVI time series trends analysis. The data are circumpolar in coverage at 8-km resolution and limited to greater than 20 degrees N.These normalized difference vegetation index (NDVI) trends were calculated using the third generation data set from the Global Inventory Modeling and Mapping Studies (GIMMS 3g). GIMMS 3g improves on its predecessor (GIMMS g) in three important ways. First, GIMMS 3g integrates data from NOAA-17 and 18 satellites to lengthen its record. Second, it addresses the spatial discontinuity north of 72 degrees N, by using SeaWiFS, in addition to SPOT VGT, to calibrate between the second and third versions of the AVHRR sensor (AVHRR/2 and AVHRR/3). Finally, the GIMMS 3g algorithm incorporates improved snowmelt detection and is calibrated based on data from the shorter, arctic growing season (May-September) rather than the entire year (January-December). 	proprietary
 GISS-CMIP5_1	GISS ModelE2 contributions to the CMIP5 archive	NCCS STAC Catalog	0850-01-01	2100-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1542315069-NCCS.umm_json	We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980-2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics.	proprietary
 GIS_EastAngliaClimateMonthly_551_1	Global Monthly Climatology for the Twentieth Century (New et al.)	ORNL_CLOUD STAC Catalog	1900-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2780535151-ORNL_CLOUD.umm_json	A 0.5 degree lat/lon data set of monthly surface climate over global land areas, excluding Antarctica. Primary variables are interpolated directly from station time-series: precipitation, mean temperature and diurnal temperature range.	proprietary
-GLAH01_033	GLAS/ICESat L1A Global Altimetry Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json	Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter.  Each data granule has an associated browse product.	proprietary
 GLAH01_033	GLAS/ICESat L1A Global Altimetry Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153547306-NSIDC_CPRD.umm_json	Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter.  Each data granule has an associated browse product.	proprietary
-GLAH02_033	GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json	GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.	proprietary
+GLAH01_033	GLAS/ICESat L1A Global Altimetry Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000400-NSIDC_ECS.umm_json	Level-1A altimetry data (GLAH01) include the transmitted and received waveform from the altimeter.  Each data granule has an associated browse product.	proprietary
 GLAH02_033	GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153547430-NSIDC_CPRD.umm_json	GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.	proprietary
+GLAH02_033	GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json	GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product.	proprietary
 GLAH03_033	GLAS/ICESat L1A Global Engineering Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json	Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.	proprietary
 GLAH03_033	GLAS/ICESat L1A Global Engineering Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json	Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02.	proprietary
-GLAH04_033	GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json	Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.	proprietary
 GLAH04_033	GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json	Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.	proprietary
-GLAH05_034	GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json	GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization).  Each data granule has an associated browse product.	proprietary
+GLAH04_033	GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json	Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.	proprietary
 GLAH05_034	GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549166-NSIDC_CPRD.umm_json	GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization).  Each data granule has an associated browse product.	proprietary
-GLAH06_034	GLAS/ICESat L1B Global Elevation Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json	GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.	proprietary
+GLAH05_034	GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json	GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization).  Each data granule has an associated browse product.	proprietary
 GLAH06_034	GLAS/ICESat L1B Global Elevation Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json	GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.	proprietary
-GLAH07_033	GLAS/ICESat L1B Global Backscatter Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json	GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.	proprietary
+GLAH06_034	GLAS/ICESat L1B Global Elevation Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json	GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product.	proprietary
 GLAH07_033	GLAS/ICESat L1B Global Backscatter Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json	GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.	proprietary
+GLAH07_033	GLAS/ICESat L1B Global Backscatter Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json	GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data.  Each data granule has an associated browse product.	proprietary
 GLAH08_033	GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json	GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate).  Each data granule has an associated browse product.	proprietary
 GLAH08_033	GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json	GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate).  Each data granule has an associated browse product.	proprietary
-GLAH09_033	GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json	GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.  Each data granule has an associated browse product.	proprietary
 GLAH09_033	GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json	GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.  Each data granule has an associated browse product.	proprietary
-GLAH10_033	GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-09-25	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json	GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds.  Each data granule has an associated browse product.	proprietary
+GLAH09_033	GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json	GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz.  Each data granule has an associated browse product.	proprietary
 GLAH10_033	GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-09-25	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json	GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds.  Each data granule has an associated browse product.	proprietary
-GLAH11_033	GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json	GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.	proprietary
+GLAH10_033	GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-09-25	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json	GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds.  Each data granule has an associated browse product.	proprietary
 GLAH11_033	GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json	GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.	proprietary
+GLAH11_033	GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json	GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.	proprietary
 GLAH12_034	GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLAH12_034	GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
-GLAH13_034	GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLAH13_034	GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
-GLAH14_034	GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
+GLAH13_034	GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLAH14_034	GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153551318-NSIDC_CPRD.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
+GLAH14_034	GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLAH15_034	GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034	NSIDC_ECS STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C1000000420-NSIDC_ECS.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLAH15_034	GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034	NSIDC_CPRD STAC Catalog	2003-02-20	2009-10-11	-180, -86, 180, 86	https://cmr.earthdata.nasa.gov/search/concepts/C2153552369-NSIDC_CPRD.umm_json	GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product.  Each data granule has an associated browse product.	proprietary
 GLCHMK_001	G-LiHT Canopy Height Model KML V001	LPCLOUD STAC Catalog	2011-06-30		-170, 10, -50, 73	https://cmr.earthdata.nasa.gov/search/concepts/C2763264695-LPCLOUD.umm_json	Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.    The purpose of G-LiHT’s Canopy Height Model Keyhole Markup Language (KML) data product (GLCHMK) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.      GLCHMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format. 	proprietary
@@ -6524,9 +6502,7 @@ GRACEDADM_CLSM0125US_7D_4.0	Groundwater and Soil Moisture Conditions from GRACE
 GRACEDADM_CLSM025GL_7D_3.0	Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation L4 7-days 0.25 x 0.25 degree Global V3.0 (GRACEDADM_CLSM025GL_7D) at GES DISC	GES_DISC STAC Catalog	2003-02-03		-180, -60, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2283200566-GES_DISC.umm_json	Scientists at NASA Goddard Space Flight Center generate groundwater and soil moisture drought indicators each week. They are based on terrestrial water storage observations derived from GRACE-FO satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.  This data product is GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM) Global Version 3.0 from a global GRACE and GRACE-FO data assimilation and drought indicator product generation (Li et al., 2019). It varies from the other GRACE-DA-DM products which are from the U.S. GRACE-based drought indicator product generation (Houborg et al., 2012). The GRACE-DA-DM Global V3.0 is similar to the GRACE-DA-DM U.S. V4.0 product. Both products are based on the Catchment Land Surface Model (CLSM) Fortuna 2.5 version simulation that was created within the Land Information System data assimilation framework (Kumar et al., 2016). GRACE-DA-DM Global V3.0 drought indicator maps are derived from the GLDAS_CLSM025_DA1_D product, at 0.25 degree resolution, forced by ECMWF meteorological data, and assimilated RL06 GRACE and GRACE-FO data from the University of Texas at Austin (Save et al., 2016; Save, 2020). The  GRACE-DA-DM U.S. V4.0 is at 0.125 degree, which is based on a model simulation (not published at GES DISC) forced by NLDAS-2 meteorological data and assimilated with RL06 GRACE/GRACE-FO data.    More information on GRACE-DA-DM U.S. V4.0 and previous versions of the data can be found in the README.  The GRACE-DA-DM Global V3.0 data product contains three drought indicators: Groundwater Percentile, Root Zone Soil Moisture Percentile, and Surface Soil Moisture Percentile. These drought indicators express wet or dry conditions as a percentile, indicating the probability of occurrence within the period of record from 1948 to 2014. The drought indicator data are daily, but available only one day (Monday) per week.  The data have a spatial resolution of 0.25 x 0.25 degree with global coverage (60S, 180W, 90N, 180E), and a temporal range from February 2003 to present (with a 3-6 month latency). The data are archived in NetCDF format.  The GRACE-DA-DM is an operational project which produces groundwater and soil moisture drought indicators each week.  The operational data is available weekly with a 2-9 day latency from the NASA GRACE project home page found under the Documentation tab.  The GRACE-DA-DM data distributed here at GESDISC is the final archive version, which is generated after the latest GRACE-FO data are available.	proprietary
 GRACEFO_L1A_ASCII_GRAV_JPL_RL04_4	GRACE-FO Level-1A Release version 4.0 from JPL in ASCII	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C2036882111-POCLOUD.umm_json	FOR EXPERT USE ONLY. The GRACE-FO Level-1A data contains telemetry data that has been converted to engineering units, from which Level-1B data products are derived. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).	proprietary
 GRACEFO_L1B_ASCII_GRAV_JPL_RL04_4	GRACE-FO Level-1B Release version 4.0 from JPL in ASCII	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C2036882118-POCLOUD.umm_json	FOR EXPERT USE ONLY. The GRACE-FO Level-1B data provide all necessary inputs to derive monthly time variations in the Earth gravity field. Level-1B data are also used for GRACE orbit and mean gravity field determination. For a detailed description, please see the GRACE-FO Level-1 documentation (https://podaac.jpl.nasa.gov/gravity/gracefo-documentation).	proprietary
-GRACEFO_L2_CSR_MONTHLY_0062_6.2	GRACE-FO Level-2  Monthly Geopotential Spherical Harmonics CSR Release 06.2 (RL06.2)	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C2753948997-POCLOUD.umm_json	FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas at Austin Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.2 data is an updated version of the initial GRACE-FO RL06 and RL06.1 Level-2 data products. RL06.2 differs from RL06.1 only in the handling of GPS data set during Level-2 processing. GPS handling for RL06.2 was re-assessed due to steady degradation in low degrees during late 2022 and 2023 in RL06.1. Alterations in the processing of the GPS data were made to increase the volume of data available after editing. Additionally relative weighting between KBR and GPS was updated after June 2020 to use optimal weighting instead of capped weights for GPS used for RL06.1 solutions. The resulting RL06.2 time series shows improvement in the low degree coefficients after 2022. All GRACE-FO RL06.2 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields. Refer to the mission page for more information.	proprietary
 GRACEFO_L2_CSR_MONTHLY_0063_6.3	GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics CSR Release 06.3 (RL06.3)	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C3059656222-POCLOUD.umm_json	FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the University of Texas (at Austin) Center for Space Research (CSR). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.	proprietary
-GRACEFO_L2_GFZ_MONTHLY_0061_6.1	GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics GFZ Release 6.1 (RL06.1)	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C2257298799-POCLOUD.umm_json	FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. This GRACE-FO RL06.1 data is an updated version of the initial GRACE-FO RL06 Level-2 data products. RL06.1 differs from RL06 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.1 uses ACH1B RL04, which replaces ACT1B RL04 that was used for Level-2 RL06. All GRACE-FO RL06.1 Level-2 products are fully compatible with the GRACE RL06 Level-2 fields.  Refer to the mission page for more information. 	proprietary
 GRACEFO_L2_GFZ_MONTHLY_0063_6.3	GRACE-FO Level-2 Monthly Geopotential Spherical Harmonics GFZ Release 6.3 (RL06.3)	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C3059674861-POCLOUD.umm_json	FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the German Research Centre for Geosciences (GFZ). The data are provided as spherical harmonic coefficients, averaged over approximately a month. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO mission. Refer to the mission page for more information.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-deadband operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.	proprietary
 GRACEFO_L2_JPL_MONTHLY_0063_6.3	GRACE-FO Level-2  Monthly Geopotential Spherical Harmonics JPL Release 6.3 (RL06.3)	POCLOUD STAC Catalog	2018-05-22		-180, -89, 180, 89	https://cmr.earthdata.nasa.gov/search/concepts/C3059700062-POCLOUD.umm_json	FOR EXPERT USE ONLY. This dataset contains estimates of the total month-by-month geopotential of the Earth, derived from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission measurements, produced by the NASA Jet Propulsion Laboratory (JPL). The data are provided as spherical harmonic coefficients, averaged over approximately a month, and available from 2018 onward. These coefficients are derived from the Microwave Instrument (MWI) measured intersatellite range changes between the twin spacecraft of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission. The GRACE-FO mission, a joint partnership between NASA and the German Research Centre for Geosciences (GFZ), launched on 22 May 2018. It uses twin satellites to accurately map variations in the Earth's gravity field and surface mass distribution. It is designed as a successor to the Gravity Recovery and Climate Experiment (GRACE) mission.  <br><br> This GRACE-FO RL06.3 data is an updated version of the GRACE-FO RL06.1 Level-2 data products. RL06.3 differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 satellite: Level-2 RL06.3 uses ACH1B RL04 that is contained within the ACX2 Level-1 bundle, which replaces ACH1B RL04 contained within the ACX Level-1 bundle that was used for Level-2 RL06.1 (note: ACX2-L1B is only applicable for 01/2023 onwards in wide-pointing operational mode; from 6/2018 through 12/2022, RL06.1 and RL06.3 GRACE-FO data are identical and based on ACX; ACX2 is not available for 03/2023-06/2023 as the satellites were not in wide-pointing mode during that period). All GRACE-FO RL06.3 Level-2 products are fully compatible with the GRACE RL06 level-2 fields. Refer to the mission page for more information.	proprietary
 GRACE_ABPR_FO_L2_V1.0_1.0	Hourly Ocean Bottom Pressure at the North Pole from the Arctic Bottom Pressure Recorder Follow On Version 1.0	POCLOUD STAC Catalog	2022-08-14	2023-08-17	50.399, 89.991, 50.399, 89.991	https://cmr.earthdata.nasa.gov/search/concepts/C3252991748-POCLOUD.umm_json	This dataset provides the first year of hourly ocean bottom pressure measurements at the North Pole from the Arctic Bottom Pressure Recorder - Follow On (ABPR-FO) deployed in August 2022. Based around a Paroscientific Digiquartz pressure sensor, the ABPR-FO is designed and powered to collect and transmit data for 5 years. The 5-year record will be collected, quality-controlled and updated at PO.DAAC on a yearly basis. The full time series is provided as a single netCDF file, and reports both bottom pressure (reported as liquid water equivalent thickness) and temperature. This dataset aims to serve as in situ validation for GRACE-FO data products in the central Arctic. This project is funded by NASA’s GRACE and GRACE-FO Science Team and supported by PONANT Science.   	proprietary
@@ -6691,9 +6667,7 @@ HAQ_TROPOMI_NO2_CONUS_S_L3_2.4	HAQAST Sentinel-5P TROPOMI Nitrogen Dioxide (NO2)
 HAQ_TROPOMI_NO2_GLOBAL_A_L3_2.4	HAQAST Sentinel-5P TROPOMI Nitrogen Dioxide (NO2) GLOBAL Annual Level 3 0.1 x 0.1 Degree Gridded Data Version 2.4  (HAQ_TROPOMI_NO2_GLOBAL_A_L3) at GES DISC	GES_DISC STAC Catalog	2019-01-01		-179.5, -60, 179.5, 75	https://cmr.earthdata.nasa.gov/search/concepts/C3087325001-GES_DISC.umm_json	This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present.   This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.  NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. 	proprietary
 HAQ_TROPOMI_NO2_GLOBAL_M_L3_2.4	HAQAST Sentinel-5P TROPOMI Nitrogen Dioxide (NO2) GLOBAL Monthly Level 3 0.1 x 0.1 Degree Gridded Data Version 2.4 (HAQ_TROPOMI_NO2_GLOBAL_M_L3) at GES DISC	GES_DISC STAC Catalog	2019-01-01		-179.5, -60, 179.5, 75	https://cmr.earthdata.nasa.gov/search/concepts/C3087325222-GES_DISC.umm_json	This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present.   This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.  NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations. 	proprietary
 HAWKEYE_L1_1	SeaHawk HawkEye Level-1 Data, version 1	OB_DAAC STAC Catalog	2018-12-03		-180, 90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2124738110-OB_DAAC.umm_json	The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).	proprietary
-HAWKEYE_L1_1	SeaHawk-1 HawkEye Level-1A  Data, version 1	OB_CLOUD STAC Catalog	2018-12-03	2023-10-27	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3160685741-OB_CLOUD.umm_json	The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide high quality, high resolution imagery (120 meter) of the open ocean, coastal zones, lakes, estuaries and land features. This ability provides a valuable complement to the lower resolution measurements from previous missions like SeaWiFS, MODIS and VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and is funded by the Moore Foundation under a grant for the Sustained Ocean Color Observations with Nanosatellites (SOCON).	proprietary
 HAWKEYE_L2_OC_2018.0	SeaHawk HawkEye Regional Ocean Color (OC) Data, version 2018.0	OB_DAAC STAC Catalog	2021-04-16	2023-10-27	-180, 90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2124738174-OB_DAAC.umm_json	The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).	proprietary
-HAWKEYE_L2_OC_2022.0	SeaHawk-1 HawkEye Level-2 Regional Ocean Color (OC) Data, version 2022.0	OB_CLOUD STAC Catalog	2019-03-21	2023-10-27	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3160685780-OB_CLOUD.umm_json	The Hawkeye instrument, flown onboard the SeaHawk CubeSat, was optimized to provide  high quality, high resolution imagery (120 meter) of the open ocean, coastal zones,  lakes, estuaries and land features. This ability provides a valuable complement to  the lower resolution measurements from previous missions like SeaWiFS, MODIS and  VIIRS. The SeaHawk CubeSat mission is a partnership between NASA and the University  of North Carolina, Wilmington (UNCW), Cloudland Instruments and AAC-Clyde Space and  is funded by the Moore Foundation under a grant for the Sustained Ocean Color  Observations with Nanosatellites (SOCON).	proprietary
 HCDN_810_1	Monthly Climate Data for Selected USGS HCDN Sites, 1951-1990, R1	ORNL_CLOUD STAC Catalog	1951-01-01	1990-12-31	-125.15, 24.16, -66.74, 49.39	https://cmr.earthdata.nasa.gov/search/concepts/C2756285170-ORNL_CLOUD.umm_json	Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990.The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999.Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable.  Please see the Data Set Revisions section of this document for detailed information. 	proprietary
 HDDS_Baseline_Adhoc_Not provided	HDDS_Baseline_Adhoc	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567950-USGS_LTA.umm_json	The U.S. Geological Survey (USGS) Emergency Operations, in support of the Department of Homeland Security, provides imagery and resources for use in disaster preparations, rescue and relief operations, damage assessments, and reconstruction efforts.  A variety of products, however ,not limited to, include:  multiple types of satellite and aerial imagery, maps, products, presentations and data source documents.	proprietary
 HE_DEM_5MIN_Not provided	5 Minute Global Land and Seafloor Elevations: Hamilton Exploration	SCIOPS STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214584956-SCIOPS.umm_json	The following text was abstracted from Bruce Gittings' Digital Elevation Data Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'.  The catalogue is a comprehensive source of information on digital elevation data and should be retrieved in its entirity for additional    information.      Global land and seafloor elevations exist... in ASCII on IBM-formatted floppy disk as a 5 degree quad at 5 arc second resolution for $75 or a one degree quad at 12 arc second resolution for $195 (designate the SW corner of the required quad in each case).  Data may be redistributed for non commercial purposes only.  The following data are available for each USGS 7.5' quadrangle.  Data is arranged and sold by layers. Files are in AutoCAD format.  Data is under copyright.       Basic roads.............. $80            Enhanced roads.......... $100      Double line roads....... $150      Geographic names......... $40      County Lines............. $80           Township Range and Section              Lines... $80      Contours................ $160            Terrain Relief Grid..... $160      Quicksurf Compatible x,y,z ascii... $160	proprietary
@@ -10592,7 +10566,7 @@ N19_AVH13C1_6	NOAA19 AVHRR Atmospherically Corrected Normalized Difference Veget
 N21-VIIRS-L2P-ACSPO-v2.80_2.80	GHRSST Level 2P NOAA ACSPO SST  v2.80 from VIIRS on NOAA-21 Satellite	POCLOUD STAC Catalog	2023-03-19		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2754895884-POCLOUD.umm_json	The N21-VIIRS-L2P-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the The Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.  <br><br>    VIIRS L2P SST products are derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system (Jonasson et al. 2022). Data are reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. In ACSPO products, SSTs are derived using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels with quality level QL=5 are recommended. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. <br><br>    The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO N21 VIIRS L3U product is also available  (10.5067/GHV21-3U280) (Ignatov et al., 2017).	proprietary
 N21-VIIRS-L3U-ACSPO-v2.80_2.80	GHRSST Level 3U NOAA ACSPO SST v2.80 from VIIRS on NOAA-21 Satellite	POCLOUD STAC Catalog	2023-03-19		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2754899545-POCLOUD.umm_json	The N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.  <br><br>    The ACSPO N21 VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N21 VIIRS L2P product, also available at PO.DAAC (10.5067/GHV21-2P280). The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The  ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. There are 144 granules per 24 hour interval, with a total data volume of 0.6GB/day. Fill values are reported at all invalid pixels, including pixels >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SST, a subset of variable l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and the SST minus reference CMC SST  (Canadian Met Centre 0.1deg L4 SST, 10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. <br><br>    The ACSPO VIIRS L3U product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM).	proprietary
 NAAMES_0	North Atlantic Aerosols and Marine Ecosystems Study	OB_DAAC STAC Catalog	2015-11-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360513-OB_DAAC.umm_json	Measurements made under the NAAMES (North Atlantic Aerosols and Marine Ecosystems Study) program.INTERNAL LINKS (Special datasets*)*Special datasets are not in SeaBASS format, and are thus only accesible via these links (they do not appear in the file search or lists of metadata)Altimetry re-analyses (maps of diagnostics e.g., integration of altimetry & maps of masks of water origin)Ship Underway Data (systems include: IMU, SAMOS, and SSW)EXTERNAL LINKSAircraft dataSatellite dataSeaBASS NAAMES pageSee URL below for the primary NAAMES website	proprietary
-NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1	NAAMES C-130 Aerosol and Cloud Remote Sensing Data, Version 1	LARC_ASDC STAC Catalog	2015-11-04	2018-03-25	-85, 25, -25, 70	https://cmr.earthdata.nasa.gov/search/concepts/C1688110309-LARC_ASDC.umm_json	NAAMES_AerosolCloud_AircraftRemoteSensing_Data are remotely sensed cloud, aerosol and ocean optical measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
+NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1	NAAMES C-130 Aerosol and Cloud Remote Sensing Data, Version 1	LARC_ASDC STAC Catalog	2015-11-04	2018-03-25	180, 25, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1688110309-LARC_ASDC.umm_json	NAAMES_AerosolCloud_AircraftRemoteSensing_Data are remotely sensed cloud, aerosol and ocean optical measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_Aerosol_AircraftInSitu_Data_1	NAAMES C-130 Aerosol In Situ , Version 1	LARC_ASDC STAC Catalog	2015-11-12	2017-09-20	-80, 30, -30, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688110103-LARC_ASDC.umm_json	NAAMES_Aerosol_AircraftInSitu_Data are in situ aerosol measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_Aerosol_ShipInSitu_Data_1	NAAMES R/V Atlantis Aerosol In Situ Data, Version 1	LARC_ASDC STAC Catalog	2015-11-06	2018-04-13	-81, 18, -29, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688110467-LARC_ASDC.umm_json	NAAMES_Aerosol_ShipInSitu_Data are in situ aerosol measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 – April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_Cloud_AircraftInSitu_Data_1	NAAMES C-130 Cloud In Situ Data, Version 1	LARC_ASDC STAC Catalog	2015-11-12	2017-09-20	-80, 30, -30, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688111638-LARC_ASDC.umm_json	NAAMES_Cloud_AircraftInSitu_Data are in situ cloud measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.     The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
@@ -10602,7 +10576,7 @@ NAAMES_MetNav_ShipInSitu_Data_1	NAAMES R/V Atlantis Navigational and Meteorologi
 NAAMES_Met_SondeInSitu_Data_1	NAAMES Sonde Meteorological InSitu Data, Version 1	LARC_ASDC STAC Catalog	2015-11-07	2016-06-03	-80, 30, 65, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688124829-LARC_ASDC.umm_json	NAAMES_Met_SondeInSitu_Data are meteorological radiosonde measurements collected via radiosonde launches during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015 and May 11, 2016 – June 5 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_Misc_Ship_Data_1	NAAMES R/V Atlantis Miscellaneous Data, Version 1	LARC_ASDC STAC Catalog	2015-01-01	2018-09-24	179, 0, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1688125085-LARC_ASDC.umm_json	NAAMES_Misc_Ship_Data are miscellaneous ship measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 – April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_Ocean_AircraftRemoteSensing_Data_1	NAAMES C-130 Ocean Remote Sensing Data, Version 1	LARC_ASDC STAC Catalog	2015-11-04	2017-09-20	180, 0, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1688126075-LARC_ASDC.umm_json	NAAMES_Ocean_AircraftRemoteSensing_Data are remotely sensed ocean measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Related ocean property measurements are available in the NAAMES_AerosolCloud_AircraftRemoteSensing_Data_1. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
-NAAMES_Radiation_AircraftInSitu_Data_1	NAAMES C-130 Aircraft In-Situ Radiation Data	LARC_ASDC STAC Catalog	2015-11-04	2017-09-20	-80, 30, -30, 65	https://cmr.earthdata.nasa.gov/search/concepts/C2031004453-LARC_ASDC.umm_json	NAAMES_Radiation_AircraftInSitu_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in-situ radiation data collected onboard the C-130 aircraft during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
+NAAMES_Radiation_AircraftInSitu_Data_1	NAAMES C-130 Aircraft In-Situ Radiation Data	LARC_ASDC STAC Catalog	2015-11-04	2017-09-22	-80, 30, -30, 65	https://cmr.earthdata.nasa.gov/search/concepts/C2031004453-LARC_ASDC.umm_json	NAAMES_Radiation_AircraftInSitu_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in-situ radiation data collected onboard the C-130 aircraft during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_TraceGas_AircraftInSitu_Data_1	NAAMES C-130 Trace Gas In Situ Data, Version 1	LARC_ASDC STAC Catalog	2015-11-12	2017-09-20	-80, 0, 0, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688126148-LARC_ASDC.umm_json	NAAMES_TraceGas_AircraftInSitu_Data are in situ trace gas measurements collected onboard the C-130 aircraft during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016 and August 30, 2017-September 22, 2017 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The airborne products link local-scale processes and properties to the larger scale continuous satellite record. Data collection for this product is complete.     The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAAMES_TraceGas_ShipInSitu_Data_1	NAAMES R/V Atlantis Trace Gas In Situ Data, Version 1	LARC_ASDC STAC Catalog	2015-11-06	2018-04-12	-80, 18, -30, 65	https://cmr.earthdata.nasa.gov/search/concepts/C1688126518-LARC_ASDC.umm_json	NAAMES_TraceGas_ShipInSitu_Data are in situ trace gas measurements collected onboard the R/V Atlantis vessel during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). These measurements were collected from November 4, 2015 – November 29, 2015, May 11, 2016 – June 5, 2016, August 30, 2017-September 22, 2017 and March 18, 2018 – April 13, 2018 over the North Atlantic Ocean. The primary objective of NAAMES was to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate. The ship-based measurements provide detailed characterization of plankton stocks, rate processes, and community composition. Ship measurements collected during NAAMES also characterize sea water volatile organic compounds, their processing by ocean ecosystems, and the concentrations and properties of gases and particles in the overlying atmosphere. Data collection for this product is complete.    The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.	proprietary
 NAB08_0	North Atlantic Bight measurements in 2008	OB_DAAC STAC Catalog	2008-04-03		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360515-OB_DAAC.umm_json	Measurements made in the North Atlantic Bight in 2008.	proprietary
@@ -11566,8 +11540,8 @@ OMCLDO2Z_003	OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) Zoomed 1-Or
 OMCLDO2_003	OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 1-Orbit L2 Swath 13x24km V003 (OMCLDO2) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966787-GES_DISC.umm_json	The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind.  The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day.	proprietary
 OMCLDO2_CPR_003	OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 200-km swath subset along CloudSat track V003 (OMCLDO2_CPR) at GES DISC	GES_DISC STAC Catalog	2006-06-01	2018-03-02	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1236350939-GES_DISC.umm_json	This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.        (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR)	proprietary
 OMCLDRRG_003	OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMCLDRRG) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136100-GES_DISC.umm_json	This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.	proprietary
-OMCLDRR_003	OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json	The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering).  This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.  The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.	proprietary
 OMCLDRR_003	OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT	OMINRT STAC Catalog	2004-07-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json	The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/	proprietary
+OMCLDRR_003	OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json	The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering).  This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.  The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.	proprietary
 OMCLDRR_004	OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3159637081-GES_DISC.umm_json	This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner.  The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.	proprietary
 OMCLDRR_CPR_003	OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 200-km swath subset along CloudSat track V003 (OMCLDRR_CPR) at GES DISC	GES_DISC STAC Catalog	2006-06-01	2018-03-02	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1236350980-GES_DISC.umm_json	This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.  (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR)	proprietary
 OMDOAO3G_003	OMI/Aura Ozone (O3) DOAS Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMDOAO3G) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136103-GES_DISC.umm_json	This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3.  This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI.  The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size.	proprietary
@@ -11644,17 +11618,17 @@ OMPS_NPP_NPBUVO3_L2_2	OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital	G
 OMPS_NPP_NPBUVO3_L2_2.9	OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital V2.9 (OMPS_NPP_NPBUVO3_L2) at GES DISC	GES_DISC STAC Catalog	2011-11-13		-180, -82, 180, 82	https://cmr.earthdata.nasa.gov/search/concepts/C2821060582-GES_DISC.umm_json	The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm.  Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5.	proprietary
 OMPS_NPP_NPEV_L1B_2	OMPS/NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital V2 (OMPS_NPP_NPEV_L1B) at GES DISC	GES_DISC STAC Catalog	2011-11-13		-180, -82, 180, 82	https://cmr.earthdata.nasa.gov/search/concepts/C1279850611-GES_DISC.umm_json	The OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite.  Each granule typically contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is nearly global (-82 to 82 degrees latitude), and there are about 14.5 orbits per day each with a single nadir measurement along the satellite track.	proprietary
 OMSO2G_003	OMI/Aura Sulphur Dioxide (SO2) Total Column Daily L2 Global Gridded 0.125 degree x 0.125 degree V3 (OMSO2G) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136113-GES_DISC.umm_json	This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2.  OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the  retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags.  The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.	proprietary
-OMSO2_003	OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json	The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources.  Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li.  The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.	proprietary
 OMSO2_003	OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT	OMINRT STAC Catalog	2004-07-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json	The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura  satellite on July 15, 2004 (1:38 pm equator crossing time, ascending  mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal  investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal  Netherlands Meteorological Institute). OMI is designed to monitor  stratospheric and tropospheric ozone, clouds, aerosols and smoke from  biomass burning, SO2 from volcanic eruptions, and key tropospheric  pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI  sensor counts, calibrated and geolocated radiances, and all derived  geophysical atmospheric products will be archived at the NASA Goddard  DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly  available from NASA GSFC Earth Sciences (GES) Data and Information  Services Center (DISC) for public access.  OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the  retrieval algorithm. It also contains quality flags, geolocation and other ancillary information.        The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu).       OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and   documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php )             For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/.       For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below.       http://disc.sci.gsfc.nasa.gov/Aura/       http://disc.gsfc.nasa.gov/acdisc/	proprietary
+OMSO2_003	OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json	The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources.  Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li.  The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.	proprietary
 OMSO2_CPR_003	OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC	GES_DISC STAC Catalog	2006-06-01	2018-03-02	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1236350970-GES_DISC.umm_json	"This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission.  The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications.  (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003)              This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2.  The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the ""zoom mode"" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values:   1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km,   4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km.   The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment).   OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes."	proprietary
 OMSO2e_003	OMI/Aura Sulfur Dioxide (SO2) Total Column Daily L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMSO2e) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136112-GES_DISC.umm_json	"The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the ""best pixel"", selected from all ""good"" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid.  The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model."	proprietary
 OMTO3G_003	OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMTO3G) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136114-GES_DISC.umm_json	This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.  The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains  UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags.  The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.	proprietary
-OMTO3_003	OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT	OMINRT STAC Catalog	2004-07-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json	The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA  for the public access.  The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that  essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/  http://www.knmi.nl/omi/research/documents/ .        Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .	proprietary
 OMTO3_003	OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json	The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia.  The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.	proprietary
+OMTO3_003	OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT	OMINRT STAC Catalog	2004-07-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json	The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA  for the public access.  The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that  essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist  Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/  http://www.knmi.nl/omi/research/documents/ .        Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .	proprietary
 OMTO3_CPR_003	OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution	GES_DISC STAC Catalog	2006-06-01	2018-03-02	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1236350982-GES_DISC.umm_json	This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications.             (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003)	proprietary
 OMTO3d_003	OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V3 (OMTO3d) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136070-GES_DISC.umm_json	The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids.  The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.	proprietary
-OMTO3e_003	OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json	The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles.  The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.	proprietary
 OMTO3e_003	OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT	OMINRT STAC Catalog	2004-07-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json	The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) .	proprietary
+OMTO3e_003	OMI/Aura TOMS-Like Ozone and Radiative Cloud Fraction L3 1 day 0.25 degree x 0.25 degree V3 (OMTO3e) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1266136071-GES_DISC.umm_json	The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles.  The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.	proprietary
 OMUANC_004	Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUANC) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2556143653-GES_DISC.umm_json	The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.  The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.  The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools.  Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.	proprietary
 OMUFPITMET_003	GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km V3 (OMUFPITMET) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1561222825-GES_DISC.umm_json	The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.    The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET.  The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used.  The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.  To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere.   The OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools.  Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.	proprietary
 OMUFPMET_004	GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km V4 (OMUFPMET) at GES DISC	GES_DISC STAC Catalog	2004-10-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2556146042-GES_DISC.umm_json	The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.  The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.  The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools.  Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.	proprietary
@@ -11673,7 +11647,7 @@ ONR_PenobscotRiverSystem_0	Office of Naval Research (ONR) measurements in the Pe
 OPERA_L2_CSLC-S1-STATIC_V1_1	OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)	ASF STAC Catalog	2014-04-03		-180, 4.9744342, -64.4225561, 71.2183663	https://cmr.earthdata.nasa.gov/search/concepts/C2795135668-ASF.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static layers generation algorithm. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset.	proprietary
 OPERA_L2_CSLC-S1_V1_1	OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)	ASF STAC Catalog	2014-06-15		-180, 4.9744342, -64.4225561, 71.2183663	https://cmr.earthdata.nasa.gov/search/concepts/C2777443834-ASF.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data, provided by the European Space Agency.  The CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively.  Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).	proprietary
 OPERA_L2_RTC-S1-STATIC_V1_1	OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)	ASF STAC Catalog	2014-04-03		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2795135174-ASF.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product.  Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm.  Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID.  The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica).  The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset.	proprietary
-OPERA_L2_RTC-S1_V1_1	OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)	ASF STAC Catalog	2023-10-04		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2777436413-ASF.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data, provided by the European Space Agency, with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product.  The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0,  obtained from the original radar brightness beta-naught, β0,  through radiometric terrain correction.  The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.	proprietary
+OPERA_L2_RTC-S1_V1_1	OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)	ASF STAC Catalog	2021-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2777436413-ASF.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data, provided by the European Space Agency, with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product.  The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0,  obtained from the original radar brightness beta-naught, β0,  through radiometric terrain correction.  The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.	proprietary
 OPERA_L3_DIST-ALERT-HLS_V1_1	OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)	LPCLOUD STAC Catalog	2022-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2746980408-LPCLOUD.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A and Sentinel-2B Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.  The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document  for a more detailed description of the individual layers provided in the DIST-ALERT product.  	proprietary
 OPERA_L3_DIST-ANN-HLS_V1_1	OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)	LPCLOUD STAC Catalog	2022-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2519119034-LPCLOUD.umm_json	The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS)  product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).  The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS).  See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product.   	proprietary
 OPERA_L3_DSWX-HLS_V1_1.0	OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)	POCLOUD STAC Catalog	2023-04-04		-180, -84, 180, 84	https://cmr.earthdata.nasa.gov/search/concepts/C2617126679-POCLOUD.umm_json	This dataset contains Level-3 Dynamic OPERA surface water extent product version 1.  The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.    To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov 	proprietary
@@ -11729,143 +11703,6 @@ P6_AWIF_STUC00GTD_1.0	Resourcesat-1 AWIFS Standard Products	ISRO STAC Catalog	20
 P6_LIS3_STUC00GTD_1.0	Resourcesat-1 LIS3 Standard Products	ISRO STAC Catalog	2003-12-10		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1373228023-ISRO.umm_json	The medium resolution multi-spectral sensor, LISS-3 operates in four spectral bands - B2, B3, B4 in visible near infrared (VNIR) and B5 in Short Wave Infrared   (SWIR) providing data with 23.5m resolution. Standard products are full scene (path-row) based geo-referenced as well as geo-orthokit  products.	proprietary
 PACE-PAX_0	The Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment	OB_DAAC STAC Catalog	2024-08-01	2024-10-15	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3268194735-OB_DAAC.umm_json	PACE-PAX SeaBASS DOI description: The Plankton, Aerosol, Cloud, ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) was a field campaign to support validation of the PACE mission through a combination of multiple platforms including aircraft (ER-2, CIRPAS Twin Otter), ships (R/V Shearwater, R/V Blissfully), autonomous ocean- and land-based instruments, and collaboration with the PACE Validation Science Teams (e.g.PVST-SBCR and PVST-CALCOFI) and PACE Vicarious Calibration systems (HyperNAV). The PACE-PAX mission took place during the month of September 2024 in Southern and Central California and nearby coastal regions.  All PACE-PAX data are to be archived in 3 main data repositories: (1) NASA AIR-LARC for all aircraft data (until final data submission in March 2025 then migrated to the Langley Airborne DAAC), (2) AERONET-MAN for Microtops data, and (3) SeaBASS for all ocean optical, biogeochemical and phytoplankton data. The SeaBASS PACE-PAX DOI is split into 4 main cruises: PACE-PAX_Shearwater (cruise_id=RFDDMM-RS), PACE-PAX_Blissfully (cruise_id=RFDDMM-RB), PACE-PAX_SBCR (cruise_id=RFMMDD-SB), PACE-PAX_CALCOFI (cruise_id=RFMMDD-CL) where DDMM, represent the day and month collection date.	proprietary
 PACE_ABSclosure_0	PACE Absorbance Closure project, Florida	OB_DAAC STAC Catalog	2017-01-09		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360584-OB_DAAC.umm_json	Measurements from the PACE Absorbance Closure project off the coast of Florida.	proprietary
-PACE_EPH_DEF_1	PACE Definitive Ephemeris Data Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020918309-OB_CLOUD.umm_json	The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. 	proprietary
-PACE_HARP2_L0_1	PACE HARP2 Level-0 Instrument Telemetry/Multi-Detector Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798249-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L0_D1_1	PACE HARP2 Level-0 Detector 1 (D1) Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798238-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L0_D2_1	PACE HARP2 Level-0 Detector 2 (D2) Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798239-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L0_D3_1	PACE HARP2 Level-0 Detector 3 (D3) Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798240-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L0_REAL_1	PACE HARP2 Level-0 Real-time Direct Transfer Mode Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798243-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles  and clouds, as well as properties of land and water surfaces. HARP2 will combine  data from multiple along-track viewing angles (up to 60), four spectral bands in the  visible and near infrared ranges, and three angles of linear polarization to measure  the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L0_SCI_1	PACE HARP2 Level-0 Science Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798245-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_AST0_2	PACE HARP2 Level-1A Acquisition Scheme Type 0 - Full-Resolution Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579577-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_AST1_2	PACE HARP2 Level-1A Acquisition Scheme Type 1 - Half-Resolution Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579667-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_AST2_2	PACE HARP2 Level-1A Acquisition Scheme Type 2 -  Science Mode (no MTDI) Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579735-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_AST3_2	PACE HARP2 Level-1A Acquisition Scheme Type 3 Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579815-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_AST4_2	PACE HARP2 Level-1A Acquisition Scheme Type 4 - Science Mode Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579888-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1A_SCI_2	PACE HARP2 Level-1A Science Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026579942-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles  and clouds, as well as properties of land and water surfaces. HARP2 will combine  data from multiple along-track viewing angles (up to 60), four spectral bands in the  visible and near infrared ranges, and three angles of linear polarization to measure  the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1B_AST4_2	PACE HARP2 Level-1B Acquisition Scheme Type 4 - Science Mode Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026580004-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1B_SCI_2	PACE HARP2 Level-1B Science Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026580118-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1C_AST4_2	PACE HARP2 Level-1C Acquisition Scheme Type 4 - Science Mode Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026580193-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles  and clouds, as well as properties of land and water surfaces. HARP2 will combine  data from multiple along-track viewing angles (up to 60), four spectral bands in the  visible and near infrared ranges, and three angles of linear polarization to measure  the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HARP2_L1C_SCI_2	PACE HARP2 Level-1C Science Data, V2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026580280-OB_CLOUD.umm_json	The Hyper-Angular Rainbow Polarimeter #2 (HARP2) instrument, flying aboard the PACE spacecraft, is a wide-angle imaging polarimeter designed to measure aerosol particles and clouds, as well as properties of land and water surfaces. HARP2 will combine data from multiple along-track viewing angles (up to 60), four spectral bands in the visible and near infrared ranges, and three angles of linear polarization to measure the microphysical properties of the atmospheric particles including their size distribution, amount, refractive indices and particle shape.	proprietary
-PACE_HKT_1	PACE Spacecraft Housekeeping, NetCDF format Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2832273136-OB_CLOUD.umm_json	The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in  February 2024. The mission will carry three instruments - one hyperspectral  radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The  range that PACE's instruments will observe includes the UV (350-400 nm),  visible (400-700 nm), and near infrared (700-885 nm), as well as several  shortwave infrared bands. 	proprietary
-PACE_HSK_1	PACE Spacecraft Housekeeping Data, V1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2869693107-OB_CLOUD.umm_json	The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission launched in February 2024. The mission will carry three instruments - one hyperspectral radiometer (OCI) and two multi-angle polarimeters (HARP2 and SPEXone). The range that PACE's instruments will observe includes the UV (350-400 nm), visible (400-700 nm), and near infrared (700-885 nm), as well as several shortwave infrared bands. 	proprietary
-PACE_OCI_L0_DARK_1	PACE OCI Level-0 Dark Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798299-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_DIAG_1	PACE OCI Level-0 Diagnostic/Calibaration Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798302-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_LIN_1	PACE OCI Level-0 Linearity Calibration Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798304-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_LUN_1	PACE OCI Level-0 Lunar Calibration Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798306-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_RAW_1	PACE OCI Level-0 Raw Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798308-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SCI_1	PACE OCI Level-0 Science Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798309-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SNAPI_1	PACE OCI Level-0 Snapshot Internal Trigger Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798315-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SNAPX_1	PACE OCI Level-0 Snapshot External Trigger Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798322-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SOLD_1	PACE OCI Level-0 Daily Solar Calibration Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798329-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SOLM_1	PACE OCI Level-0 Monthly Solar Calibration Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798338-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_SPEC_1	PACE OCI Level-0 Spectral Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798347-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L0_STAT_1	PACE OCI Level-0 Static Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2804798354-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L1A_SCI_2	PACE OCI Level-1A Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026581050-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L1B_SCI_2	PACE OCI Level-1B Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026581092-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L1C_SCI_2	PACE OCI Level-1C Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026581150-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_AER_DB_2.0	PACE OCI Level-2 Regional Aerosol Optical Properties, Deep Blue Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920056-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_AER_DB_NRT_2.0	PACE OCI Level-2 Regional Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920010-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_AER_DT_2.0	PACE OCI Level-2 Regional Aerosol Optical Properties, Dark Target Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920135-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_AER_DT_NRT_2.0	PACE OCI Level-2 Regional Aerosol Optical Properties, Dark Target - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920095-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_AOP_2.0	PACE OCI Level-2 Regional Apparent Optical Properties Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920242-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_AOP_NRT_2.0	PACE OCI Level-2 Regional Apparent Optical Properties - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920190-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_BGC_2.0	PACE OCI Level-2 Regional Biogeochemical Properties Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920337-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_BGC_NRT_2.0	PACE OCI Level-2 Regional Biogeochemical Properties, Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920290-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_CLDMSK_NRT_2.0	PACE OCI Level-2 Regional Cloud Mask - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920380-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_CLD_2.0	PACE OCI Level-2 Regional Cloud Optical Properties Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920454-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_CLD_NRT_2.0	PACE OCI Level-2 Regional Cloud Optical Properties - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920417-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_IOP_2.0	PACE OCI Level-2 Regional Inherent Optical Properties (IOP) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920547-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_IOP_NRT_2.0	PACE OCI Level-2 Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920493-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_LAND_2.0	PACE OCI Level-2 Regional Normalized Difference Vegetation Index Land Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920658-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_LAND_NRT_2.0	PACE OCI Level-2 Regional Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920606-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_PAR_2.0	PACE OCI Level-2 Regional Photosynthetically Active Radiation (PAR) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920783-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_PAR_NRT_2.0	PACE OCI Level-2 Regional Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920715-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L2_SFREFL_2.0	PACE OCI Level-2 Regional Surface Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920963-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L2_SFREFL_NRT_2.0	PACE OCI Level-2 Regional Surface Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020920858-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DBL_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Land Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921155-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DBL_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Land - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921049-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DBO_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Ocean Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921424-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DBO_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Ocean - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921356-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DB_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921490-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DB_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921257-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DTL_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Land Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921591-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DTL_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Land - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921529-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DTO_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Ocean Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921819-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DTO_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Ocean - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921758-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AER_DT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921884-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AER_DT_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Properties, Dark Target - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921665-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AOT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Thickness Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921996-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AOT_NRT_2.0	PACE OCI Level-3 Global Binned Aerosol Optical Thickness (AOT), Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020921937-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_AVW_2.0	PACE OCI Level-3 Global Binned Apparent Visible Wavelength (AVW) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922135-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_AVW_NRT_2.0	PACE OCI Level-3 Global Binned Apparent Visible Wavelength (AVW) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922066-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_CARBON_2.0	PACE OCI Level-3 Global Binned Carbon Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922231-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_CARBON_NRT_2.0	PACE OCI Level-3 Global Binned Carbon, Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922208-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_CHL_2.0	PACE OCI Level-3 Global Binned Chlorophyll (CHL) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922315-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_CHL_NRT_2.0	PACE OCI Level-3 Global Binned Chlorophyll (CHL) - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922264-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_CLD_TOP_2.0	PACE OCI Level-3 Global Binned Cloud Optical Properties, Cloud Top Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922412-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_CLD_TOP_NRT_2.0	PACE OCI Level-3 Global Binned Cloud Optical Properties, Cloud Top - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922362-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_FLH_2.0	PACE OCI Level-3 Global Binned Fluorescence Line Height (FLH) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922494-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_FLH_NRT_2.0	PACE OCI Level-3 Global Binned Fluorescence Line Height (FLH) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922458-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_IOP_2.0	PACE OCI Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922583-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_IOP_NRT_2.0	PACE OCI Level-3 Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922543-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_KD_2.0	PACE OCI Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922664-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_KD_NRT_2.0	PACE OCI Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922624-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_LAND_2.0	PACE OCI Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922842-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_LAND_NRT_2.0	PACE OCI Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922797-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_PAR_2.0	PACE OCI Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922915-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_PAR_NRT_2.0	PACE OCI Level-3 Global Binned Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922875-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_PIC_2.0	PACE OCI Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922977-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_PIC_NRT_2.0	PACE OCI Level-3 Global Binned Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020922940-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_POC_2.0	PACE OCI Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923051-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_POC_NRT_2.0	PACE OCI Level-3 Global Binned Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923019-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_RRS_2.0	PACE OCI Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923122-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_RRS_NRT_2.0	PACE OCI Level-3 Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923086-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3B_SFREFL_2.0	PACE OCI Level-3 Global Binned Surface Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923200-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3B_SFREFL_NRT_2.0	PACE OCI Level-3 Global Binned Surface Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923163-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DBL_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Land Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923267-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DBL_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Land - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923237-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DBO_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Ocean Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923375-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DBO_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Ocean - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923343-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DB_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923412-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DB_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Deep Blue - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923313-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DTL_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Land Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923480-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DTL_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Land - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923448-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DTO_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Ocean Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923609-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DTO_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Ocean - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923569-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AER_DT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923646-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AER_DT_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Properties, Dark Target - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923531-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AOT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Thickness Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923739-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AOT_NRT_2.0	PACE OCI Level-3 Global Mapped Aerosol Optical Thickness (AOT), Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923694-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_AVW_2.0	PACE OCI Level-3 Global Mapped Apparent Visible Wavelength (AVW) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923814-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_AVW_NRT_2.0	PACE OCI Level-3 Global Mapped Apparent Visible Wavelength (AVW) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923779-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_CARBON_2.0	PACE OCI Level-3 Global Mapped Carbon Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923871-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_CARBON_NRT_2.0	PACE OCI Level-3 Global Mapped Carbon, Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923838-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_CHL_2.0	PACE OCI Level-3 Global Mapped Chlorophyll (CHL) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923954-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_CHL_NRT_2.0	PACE OCI Level-3 Global Mapped Chlorophyll (CHL) - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923919-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_CLD_TOP_2.0	PACE OCI Level-3 Global Mapped Cloud Optical Properties, Cloud Top Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924017-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_CLD_TOP_NRT_2.0	PACE OCI Level-3 Global Mapped Cloud Optical Properties, Cloud Top - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020923989-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_FLH_2.0	PACE OCI Level-3 Global Mapped Fluorescence Line Height (FLH) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924092-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_FLH_NRT_2.0	PACE OCI Level-3 Global Mapped Fluorescence Line Height (FLH) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924054-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_IOP_2.0	PACE OCI Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924182-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_IOP_NRT_2.0	PACE OCI Level-3 Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924144-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_KD_2.0	PACE OCI Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924251-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_KD_NRT_2.0	PACE OCI Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924216-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_LAND_2.0	PACE OCI Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924359-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_LAND_NRT_2.0	PACE OCI Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924301-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_PAR_2.0	PACE OCI Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924438-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_PAR_NRT_2.0	PACE OCI Level-3 Global Mapped Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924399-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_PIC_2.0	PACE OCI Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924510-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_PIC_NRT_2.0	PACE OCI Level-3 Global Mapped Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924478-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_POC_2.0	PACE OCI Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924599-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_POC_NRT_2.0	PACE OCI Level-3 Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924558-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_RRS_2.0	PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924694-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI). It is a highly advanced optical spectrometer that will be used to measure properties of light over portions of the electromagnetic spectrum. It will enable continuous measurement of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_RRS_NRT_2.0	PACE OCI Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924646-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_OCI_L3M_SFREFL_2.0	PACE OCI Level-3 Global Mapped Surface Reflectance Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924782-OB_CLOUD.umm_json	The primary sensor aboard the PACE spacecraft is the Ocean Color Instrument (OCI).  It is a highly advanced optical spectrometer that will be used to measure properties  of light over portions of the electromagnetic spectrum. It will enable continuous  measurement of light at finer wavelength resolution than previous NASA satellite  sensors, extending key system ocean color data records for climate studies.	proprietary
-PACE_OCI_L3M_SFREFL_NRT_2.0	PACE OCI Level-3 Global Mapped Surface Reflectance - Near Real-time (NRT) Data, version 2.0	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3020924734-OB_CLOUD.umm_json	The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.	proprietary
-PACE_SPEXONE_L0_1	PACE SPEXone Level-0  Data, version 1	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2816780240-OB_CLOUD.umm_json	The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.	proprietary
-PACE_SPEXONE_L1A_SCI_2	PACE SPEXone Level-1A Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026586666-OB_CLOUD.umm_json	The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.	proprietary
-PACE_SPEXONE_L1B_SCI_2	PACE SPEXone Level-1B Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026586707-OB_CLOUD.umm_json	The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.	proprietary
-PACE_SPEXONE_L1C_SCI_2	PACE SPEXone Level-1C Science Data, version 2	OB_CLOUD STAC Catalog	2024-02-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3026586744-OB_CLOUD.umm_json	The Spectro-polarimeter for Planetary Exploration one (SPEXone) instrument flying aboard the PACE spacecraft is a multi-angle polarimeter. It measures the intensity, Degree of Linear Polarization (DoLP) and Angle of Linear Polarization (AoLP) of sunlight reflected back from Earth's atmosphere, land surface, and ocean. The focus of the SPEXone development is to achieve a very high accuracy of DoLP measurements, which facilitates accurate characterization of aerosols in the atmosphere.	proprietary
 PAC_DFO_PAR_BUOY_0	PAR data from buoys along the west coast of Canada	OB_DAAC STAC Catalog	1998-05-05		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360580-OB_DAAC.umm_json	Optical sensors were added to some of the 17 meteorological ODAS (Ocean Data Acquisition System) buoys which provide weather and ocean data along and off the west coast of Canada for the Environment (EC) and the Fisheries and Ocean departments (DFO) of the Canadian federal government. This dataset includes Photosynthetically Active Radiation (PAR) data from two locations: Halibut Bank (buoy code 46146) and Saanich Inlet near the Institute of Ocean Sciences (buoy code 46134). For additional information please refer to Gower et al., 1999 (DOI:10.1109/OCEANS.1999.800169)	proprietary
 PAD_2011_1133_2	Water Quality and Spectral Reflectance, Peace-Athabasca Delta, Canada, 2010-2011	ORNL_CLOUD STAC Catalog	2010-06-21	2011-07-06	-111.93, 58.39, -110.84, 58.99	https://cmr.earthdata.nasa.gov/search/concepts/C2756238692-ORNL_CLOUD.umm_json	The Peace-Athabasca Delta (PAD) is a hydrologically complex and ecologically diverse freshwater delta formed by the confluence of the Peace, Athabasca, and Birch Rivers near the western end of Lake Athabasca, Alberta, Canada. This data set includes 3 comma-delimited ASCII files: one containing water quality data and site characteristics from June and July 2010, a second containing water quality data and site characteristics for June and July 2011, and a third containing spectral reflectance of the water surface for 2011. The 2010 data file has measurements from 62 unique sites, the majority of which were revisited in 2011. Both of the 2011 data files have measurements from 99 unique sites visited 1-4 times. 	proprietary
 PAD_935_1	Surface Water Elevation and Quality, Peace-Athabasca Delta, Canada, 2006-2007	ORNL_CLOUD STAC Catalog	2006-06-03	2007-09-10	-111.71, 58.35, -111.11, 58.65	https://cmr.earthdata.nasa.gov/search/concepts/C2756235671-ORNL_CLOUD.umm_json	The Peace-Athabasca Delta (PAD) is a large boreal wetland located in northeastern Alberta, Canada at the confluence of the Peace and Athabasca Rivers with Lake Athabasca (Figures 1 and 2). A Ramsar Convention wetland and UNESCO World Heritage Site, it is among the world's most ecologically significant wetlands.  This data set contains four comma-delimited ASCII files, two of which contain water surface elevation site and measurement information and two contain water quality and ancillary parameter location and measurement data for 120 sites within the PAD.Data archived include water surface elevation and water quality parameters measured at points throughout the Delta during summers 2006 and 2007. These data sets were originally collected to improve understanding of hydrologic recharge processes in low-relief environments and to provide ground-based measurements to validate satellite observations of inundation and sediment transport. All work was supported by the NASA Terrestrial Hydrology Program under grant NNG06GE05G to the Department of Geography, University of California-Los Angeles, Los Angeles, California. 	proprietary
@@ -12110,11 +11947,7 @@ RoyalPenguin1955-1969_1	Breeding biology of the Royal Penguin (Eudypted chrysolo
 Ruker_rymill_sat_1	Mount Ruker and Mount Rymill Satellite Image Maps 1:100 000	AU_AADC STAC Catalog	1989-03-18	1989-11-29	63, -74, 66.67, -72.67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311244-AU_AADC.umm_json	Two satellite images maps of Mt Ruker and Mt Rymill in the Australian Antarctic Territory were produced by the Australian Antarctic Division in 1998. Both maps are at a scale of 1:100 000 using Landsat TM imagery.  Data source:  Mount Ruker - Landsat TM imagery, scenes 128/112, acquired 29 November 1989.  Mount Rymill - Landsat TM imagery, scenes 128/111 and 128/112, acquired 18 March 1989 and 29 November 1989 respectively.  Nomenclature: Names have been approved by the Antarctic Names Committee of Australia. Please see the URL link for details on the images and processes used to produce these maps.	proprietary
 Russian_Forest_Disturbance_1294_1	Russian Boreal Forest Disturbance Maps Derived from Landsat Imagery, 1984-2000	ORNL_CLOUD STAC Catalog	1984-06-01	2000-08-31	30.98, 43.76, 138.63, 65.32	https://cmr.earthdata.nasa.gov/search/concepts/C2773247983-ORNL_CLOUD.umm_json	This data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period. Forest pixels were classified by year of latest disturbance or as undisturbed.	proprietary
 Rwanda Field Boundary Competition Dataset_1	Rwanda Field Boundary Competition Dataset	MLHUB STAC Catalog	2020-01-01	2023-01-01	30.2918386, -1.5902813, 30.4235458, -1.3915649	https://cmr.earthdata.nasa.gov/search/concepts/C2781412768-MLHUB.umm_json	This dataset contains field boundaries for smallholder farms in eastern Rwanda. The Nasa Harvest program funded a team of annotators from TaQadam to label Planet imagery for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet's NICFI program. Planet's basemap imagery is provided for six months (March, April, August, October, November and December). The paired dataset is provided in 256x256 chips for a total of 70 tiles covering 1532 individual fields.<br><br>Input imagery consists of a time series of planet Basemaps from the NICFI program (monthly composite) data.<br><br>Imagery Copyright 2021 Planet Labs Inc. All use subject to the Participant License Agreement.	proprietary
-S2-16D-2_NA	Sentinel-2/MSI - Level-2A - Data Cube - LCF 16 days	INPE STAC Catalog	2017-01-01	2024-06-08	-74.871069, -34.6755646, -28.006208, 5.763264	https://cmr.earthdata.nasa.gov/search/concepts/C3108204485-INPE.umm_json	Earth Observation Data Cube generated from Copernicus Sentinel-2/MSI Level-2A product over Brazil. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid Version 2 (BDC_SM V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.	proprietary
 S2K_EACM_Subset_623_1	SAFARI 2000 Monthly Climatology for the 20th Century (New et al.)	ORNL_CLOUD STAC Catalog	1901-01-01	1996-12-31	5, -5, 60, 35	https://cmr.earthdata.nasa.gov/search/concepts/C2796825238-ORNL_CLOUD.umm_json	This is a data set of mean monthly surface climate data over Southern Africa for nearly all of the twentieth century. The data set is gridded at 0.5-degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency.	proprietary
-S2_L1C_BUNDLE-1_NA	Sentinel-2 - Level-1C	INPE STAC Catalog	2017-07-16	2024-06-17	-76.19946, -34.425382, -27.861688, 41.5467078	https://cmr.earthdata.nasa.gov/search/concepts/C3108204499-INPE.umm_json	Copernicus Sentinel-2/MSI Level-1C product over Brazil. Level-1C product provides orthorectified Top-Of-Atmosphere (TOA) reflectance images.	proprietary
-S2_L2A-1_NA	Sentinel-2 - Level-2A - Cloud Optimized GeoTIFF	INPE STAC Catalog	2022-01-01	2024-06-16	-74.09735, -34.425382, -27.861688, 5.428219	https://cmr.earthdata.nasa.gov/search/concepts/C3108204483-INPE.umm_json	Copernicus Sentinel-2/MSI Level-2A product over Brazil. Level-2A product provides orthorectified surface reflectance images (Bottom-Of-Atmosphere - BOA). This dataset is provided as Cloud Optimized GeoTIFF (COG).	proprietary
-S2_L2A_BUNDLE-1_NA	Sentinel-2 - Level-2A	INPE STAC Catalog	2021-08-19	2024-06-16	-74.09735, -34.425382, -27.861688, 5.428219	https://cmr.earthdata.nasa.gov/search/concepts/C3108204134-INPE.umm_json	Copernicus Sentinel-2/MSI Level-2A product over Brazil. Level-2A product provides orthorectified surface reflectance images (Bottom-Of-Atmosphere - BOA).	proprietary
 S3A_OL_1_EFR_1	OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance	LAADS STAC Catalog	2016-04-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1286874966-LAADS.umm_json	"The OLCI/Sentinel-3A L1 Full Resolution Top of Atmosphere Reflectance product, S3A_OL_1_EFR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath.     For more information about the instrument and the mission, visit ""Sentinel Online"" at https://sentinel.esa.int/web/sentinel/home.     The S3A_OL_1_EFR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.      The OL_1_EFR product package is described below:    Element name 	             Description  Manifest.safe 	        SENTINEL-SAFE product manifest  Oa##_radiance.nc 	Radiance for OLCI acquisition bands 01 to 21  Removed_pixels.nc 	Removed pixels information needed for Level-1C generation  Time_coordinates.nc 	Time stamp annotations  Geo_coordinates.nc 	High resolution georeferencing data  Quality_flags.nc 	Classification and quality flags  Tie_geo_coordinates.nc 	Low resolution georeferencing data  Tie_geometries.nc 	Sun and view angles  Tie_meteo.nc 	        ECMWF meteorology data  Instrument_data.nc 	Instrument data    note: Oa## represents all the OLCI channels (Oa1 to Oa21).      For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci."	proprietary
 S3A_OL_1_ERR_1	OLCI/Sentinel-3A L1 Reduced Resolution Top of Atmosphere Reflectance	LAADS STAC Catalog	2016-04-25		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1286876651-LAADS.umm_json	"The OLCI/Sentinel-3A L1 Reduced Resolution Top of Atmosphere Reflectance, S3A_OL_1_ERR is generated from the data aquired by the Ocean and Land Colour Instrument (OLCI) on board European Earth Observation satellite mission, SENTINEL-3. The OLCI is a push-broom imaging spectrometer that measures solar radiation reflected by the Earth at a ground spatial resolution of around 300m, over all surfaces, in 21 spectral bands. OLCI is based on the imaging design of ENVISAT's Medium Resolution Imaging Spectrometer (MERIS). It has a 1270km wide swath.     For more information about the instrument and the mission, visit ""Sentinel Online"" at https://sentinel.esa.int/web/sentinel/home.     The S3A_OL_1_ERR is a Level-1B product. This is composed of an information package map, called a manifest, 22 measurement data files, and seven annotation data files. The 21 measurement data files (one for each band) consist of Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 Micro meter-1), georeferenced onto the Earth's surface, and spatially resampled onto an evenly spaced grid. Seven annotation files provide information on illumination and observation geometry, environment data (meteorological data) and quality and classification flags. Both measurement data files and annotation data files are written in netCDF 4 format. The manifest file is in XML format and contains metadata associated with the instrument and the processing. The S3A_OL_1_EFR is generated in Earth Observation (EO) processing mode and all parameters in this product are provided for each re-gridded pixel on the product image and for each removed pixel.      The OL_1_EFR product package is described below:    Element name 	             Description  Manifest.safe 	        SENTINEL-SAFE product manifest  Oa##_radiance.nc 	Radiance for OLCI acquisition bands 01 to 21  Time_coordinates.nc 	Time stamp annotations  Geo_coordinates.nc 	High resolution georeferencing data  Quality_flags.nc 	Classification and quality flags  Tie_geo_coordinates.nc 	Low resolution georeferencing data  Tie_geometries.nc 	Sun and view angles  Tie_meteo.nc 	        ECMWF meteorology data  Instrument_data.nc 	Instrument data    note: Oa## represents all the OLCI channels (Oa1 to Oa21).      For more information about the product, read the SENTINEL-3 OLCI User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci"	proprietary
 S3A_SL_1_RBT_1	SLSTR/Sentinel-3A L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature	LAADS STAC Catalog	2016-04-20		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1287064221-LAADS.umm_json	"The SLSTR/Sentinel-3A L1 Full Resolution Top of Atmosphere Radiances and Brightness Temperature product with shortname S3A_SL_1_RBT, is generated from the data aquired by the Sea and Land Surface Temperature Radiometer (SLSTR), on-board SENTINEL-3, is a dual scan temperature radiometer. The principal aim of the SLSTR instrument is to maintain continuity with the AATSR series of instruments. The SLSTR instrument design incorporates the basic functionality of AATSR in addition to new, more advanced features including a wider swath, new channels (including two channels dedicated to fire detection), and higher resolution in some channels. The principal objective of SLSTR products is to provide global and regional Sea and Land Surface Temperature (SST, LST) to a very high level of accuracy (better than 0.3 K) for both climatological and meteorological applications.    For more information about the instrument and the mission, visit ""Sentinel Online"" at https://sentinel.esa.int/web/sentinel/home.     The S3A_SL_1_RBT is a Level 1B product which consist of full resolution, geolocated, co-located nadir and along track view, Top of Atmosphere (TOA) brightness temperatures (in the case of thermal IR channels) or radiances (in the case of visible, NIR and SWIR channels) from all SLSTR channels. It also contains quality flags, pixel classification information and meteorological annotations. Based on components activated by configuration which are not part of the operational production baseline, the S3A_SL_1_RBT may contain 77 or 111 files. Out of the these files, 22 or 34 files contain the actual measurements, where the other 54 or 76 files contain the annotations data.    For more information about the product, read the SENTINEL-3 SLSTR User Guide at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr"	proprietary
@@ -12680,6 +12513,8 @@ SNEX23_SSA_SO_1	SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-of
 SNEX23_SWE_1	SnowEx23 Snow Water Equivalent V001	NSIDC_ECS STAC Catalog	2023-03-13	2023-03-16	-149.494, 64.8677, -147.6745, 68.615	https://cmr.earthdata.nasa.gov/search/concepts/C3041011983-NSIDC_ECS.umm_json	This data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska.	proprietary
 SNEX23_UW_GPR_1	SnowEx23 University of Wyoming Ground Penetrating Radar V001	NSIDC_ECS STAC Catalog	2023-03-08	2023-03-14	-149.54981, 68.58784, -148.61304, 70.0846	https://cmr.earthdata.nasa.gov/search/concepts/C2917647163-NSIDC_ECS.umm_json	This data set contains the results of 1 GHz ground-penetrating radar surveys conducted at the Arctic Coastal Plain (ACP) site and the Upper Kuparuk/Toolik (UKT) site in northern Alaska during the SnowEx23 field campaign. Data include two-way travel time, derived snow depth, and derived snow water equivalent. Data were collected between 8 - 14 March 2023.	proprietary
 SNEX_HRSI_SD_DEM_CO_1	SnowEx Colorado 3M Snow Depth Time Series and DEMs from High-Resolution Satellite Image Pairs V001	NSIDC_ECS STAC Catalog	2016-02-19	2022-03-02	-108.382269, 38.785954, -107.747336, 39.216156	https://cmr.earthdata.nasa.gov/search/concepts/C2849456374-NSIDC_ECS.umm_json	This data set contains a time series of snow depth maps and related intermediary snow-on and snow-off DEMs for Grand Mesa, Colorado derived from very-high-resolution (VHR) satellite stereo images and lidar point cloud data. Two of the snow depth maps coincide temporally with the 2017 NASA SnowEx Grand Mesa field campaign, providing a comparison between the satellite derived snow depth and in-situ snow depth measurements. The VHR stereo images were acquired each year between 2016 and 2022 during the approximate timing of peak snow depth by the Maxar WorldView-2, WorldView-3, and CNES/Airbus Pléiades-HR 1A and 1B satellites, while lidar data was sourced from the USGS 3D Elevation Program.	proprietary
+SNEX_MCS_Lidar_1	SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey V001	NSIDC_ECS STAC Catalog	2021-03-10	2024-04-18	-115.7304987, 43.9136453, -115.6424377, 43.9748021	https://cmr.earthdata.nasa.gov/search/concepts/C3242734666-NSIDC_ECS.umm_json	"The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as  <a href=""https://nsidc.org/data/SNEX_MCS_Lidar_Raw"">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1</a>) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign."	proprietary
+SNEX_MCS_Lidar_Raw_1	SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw V001	NSIDC_ECS STAC Catalog	2021-03-10	2024-04-18	-115.7304987, 43.9136453, -115.6424377, 43.9748021	https://cmr.earthdata.nasa.gov/search/concepts/C3267588841-NSIDC_ECS.umm_json	"The data set described here provides raw lidar data collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.   Digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as <a href=""https://nsidc.org/data/SNEX_MCS_Lidar"">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1</a>."	proprietary
 SNEX_Met_1	SnowEx Meteorological Station Measurements from Grand Mesa, CO V001	NSIDC_ECS STAC Catalog	2016-10-09	2022-08-30	-108.21399, 39.03388, -107.88383, 39.10358	https://cmr.earthdata.nasa.gov/search/concepts/C2555433963-NSIDC_ECS.umm_json	This dataset contains meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling. Each station collects a suite of meteorological data at a fixed geographic point, from varying heights above and below the surface elevation. Measured data include: air temperature, relative humidity, long and shortwave solar radiation, barometric pressure, soil moisture and temperature, and derived snow depth. The temporal data coverage varies between each station, but spans October 2016 to August 2022.      The dataset(s) contain air temperature and relative humidity (10ft and 20ft levels),  4-component radiation (shortwave, longwave), barometric pressure, soil-moisture and temperature (three depths), and a snow-depth product. Data coverage varies for each met station, but spans the time period of October 2016 – August, 2022. The data frequency is hourly and times are in UTC. The data is monotonic (no duplicate or mis-orderd timestamps) and steps have been taken to remove erroneous data. Periods of missing data are filled with NaN values. The scripts used to process raw data into the current format  are available on the github page (https://github.com/wrudisill/GrandMesaMetData/blob/main/process_data_initial.py).	proprietary
 SNEX_Met_Raw_1	SnowEx Meteorological Station Measurements from Grand Mesa, CO Raw V001	NSIDC_ECS STAC Catalog	2016-10-09	2022-08-30	-108.21399, 39.03388, -107.88383, 39.10358	https://cmr.earthdata.nasa.gov/search/concepts/C2556535183-NSIDC_ECS.umm_json	This dataset contains raw meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling	proprietary
 SNF_ASP_CVR_140_1	Aspen Forest Cover by Stratum/Plot (SNF)	ORNL_CLOUD STAC Catalog	1984-05-10	1984-06-12	-92.51, 47.66, -91.77, 48.17	https://cmr.earthdata.nasa.gov/search/concepts/C2884971276-ORNL_CLOUD.umm_json	Average percent coverage and standard deviation of each canopy stratum from subplots at each aspen site during the SNF study in the Superior National Forest, Minnesota	proprietary
@@ -13153,54 +12988,25 @@ Scotts_Fuel_1	Composition and origin of fuel from the hut of explorer Robert Fal
 Sea2Space_0	Sea to Space expedition	OB_DAAC STAC Catalog	2017-01-26		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1647028412-OB_DAAC.umm_json	Measurements from the Sea2Space (Sea to Space Particle Investigation) project aboard the RV Falkor, supported by the Schmidt Ocean Institute, in the central and northeast Pacific.	proprietary
 SeaSat.ESA.archive_NA	SeaSat ESA archive	ESA STAC Catalog	1978-07-13	1978-10-10	-125, -10, 20, 70	https://cmr.earthdata.nasa.gov/search/concepts/C1532648156-ESA.umm_json	"This collection gives access to the complete SEASAT dataset acquired by ESA and mainly covers Europe. The dataset comprises some of the first ever SAR data recorded for scientific purposes, reprocessed with the most recent processor. The Level-1 products are available as: •	SAR Ellipsoid Geocoded Precision Image •	SAR Precision Image •	SAR Single Look Complex Image  European Space Agency, Seasat SAR Precision Image. Version 1.0. https://doi.org/10.5270/SE1-99j66hv European Space Agency, Seasat SAR Single Look Complex. Version 1.0. https://doi.org/10.5270/SE1-4uij92n European Space Agency, Seasat SAR Ellipsoid Geocoded Precision Image . Version 1.0. https://doi.org/10.5270/SE1-ungwqxv"	proprietary
 SeaWiFS_L1_GAC_1	OrbView-2 SeaWiFS Global Area Coverage (GAC) Data, version 1	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2479674009-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L1_GAC_2	OrbView-2 SeaWiFS Level-1A Global Area Coverage (GAC) Data, version 2	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3202004220-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L1_MLAC_1	OrbView-2 SeaWiFS Merged Local Area Coverage (MLAC) Data, version 1	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2479842169-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L1_MLAC_2	OrbView-2 SeaWiFS Level-1A Merged Local Area Coverage (MLAC) Data, version 2	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3202004252-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L2_GAC_IOP_2022.0	OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Inherent Optical Properties (IOP) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3198658845-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L2_GAC_IOP_R2022.0	OrbView-2 SeaWiFS Regional Global Area Coverage (GAC) Inherent Optical Properties (IOP) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2455809513-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L2_GAC_OC_2022.0	OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Ocean Color (OC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789774382-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L2_GAC_OC_R2022.0	OrbView-2 SeaWiFS Regional Global Area Coverage (GAC) Ocean Color (OC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2455809652-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L2_LAND_2022.0	OrbView-2 SeaWiFS Level-2 Regional Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113252901-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L2_MLAC_IOP_2022.0	OrbView-2 SeaWiFS Level-2 Regional Merged Local Area Coverage (MLAC) Inherent Optical Properties (IOP) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3198658953-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L2_MLAC_IOP_R2022.0	OrbView-2 SeaWiFS Regional Merged Local Area Coverage (MLAC) Inherent Optical Properties (IOP) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2455809998-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L2_MLAC_OC_2022.0	OrbView-2 SeaWiFS Level-2 Regional Merged Local Area Coverage (MLAC) Ocean Color (OC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3198658688-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L2_MLAC_OC_R2022.0	OrbView-2 SeaWiFS Regional Merged Local Area Coverage (MLAC) Ocean Color (OC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2455810221-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_CHL_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113253100-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_CHL_R2022.0	OrbView-2 SeaWiFS Global Binned Chlorophyll (CHL) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645587-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_GSM_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Garver-Siegel-Maritorena Model (GSM) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113253238-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_IOP_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113253547-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_IOP_R2022.0	OrbView-2 SeaWiFS Global Binned Inherent Optical Properties (IOP) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645589-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_KD_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113253709-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_KD_R2022.0	OrbView-2 SeaWiFS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645590-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_LAND_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113253873-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_PAR_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254062-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_PAR_R2022.0	OrbView-2 SeaWiFS Global Binned Photosynthetically Available Radiation (PAR) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645593-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_PIC_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254253-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_PIC_R2022.0	OrbView-2 SeaWiFS Global Binned Particulate Inorganic Carbon (PIC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645594-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_POC_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254405-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_POC_R2022.0	OrbView-2 SeaWiFS Global Binned Particulate Organic Carbon (POC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645595-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_QAA_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Quasi-Analytical Algorithm (QAA) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254493-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_RRS_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254561-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3b_RRS_R2022.0	OrbView-2 SeaWiFS Global Binned Remote-Sensing Reflectance (RRS) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645597-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3b_ZLEE_2022.0	OrbView-2 SeaWiFS Level-3 Global Binned Euphotic Depth (ZLEE) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254626-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_CHL_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254690-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_CHL_R2022.0	OrbView-2 SeaWiFS Global Mapped Chlorophyll (CHL) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645599-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_GSM_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Garver-Siegel-Maritorena Model (GSM) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254743-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_IOP_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254783-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_IOP_R2022.0	OrbView-2 SeaWiFS Global Mapped Inherent Optical Properties (IOP) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645601-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_KD_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254820-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_KD_R2022.0	OrbView-2 SeaWiFS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645602-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_LAND_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254856-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_PAR_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254885-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_PAR_R2022.0	OrbView-2 SeaWiFS Global Mapped Photosynthetically Available Radiation (PAR) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645604-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_PIC_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254916-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_PIC_R2022.0	OrbView-2 SeaWiFS Global Mapped Particulate Inorganic Carbon (PIC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645605-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_POC_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254949-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_POC_R2022.0	OrbView-2 SeaWiFS Global Mapped Particulate Organic Carbon (POC) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645607-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_QAA_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Quasi-Analytical Algorithm (QAA) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113254974-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_RRS_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113255003-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L3m_RRS_R2022.0	OrbView-2 SeaWiFS Global Mapped Remote-Sensing Reflectance (RRS) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1449645609-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
-SeaWiFS_L3m_ZLEE_2022.0	OrbView-2 SeaWiFS Level-3 Global Mapped Euphotic Depth (ZLEE) Data, version 2022.0	OB_CLOUD STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3113255050-OB_CLOUD.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L4b_GSM_R2022.0	OrbView-2 SeaWiFS 4B Global Binned Garver-Siegel-Maritorena Model (GSM) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2802700401-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 SeaWiFS_L4m_GSM_R2022.0	OrbView-2 SeaWiFS 4M Global Mapped Garver-Siegel-Maritorena Model (GSM) Data, version R2022.0	OB_DAAC STAC Catalog	1997-09-04	2010-12-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2802700402-OB_DAAC.umm_json	The SeaWiFS instrument was launched by Orbital Sciences Corporation on the OrbView-2 (a.k.a. SeaStar) satellite in August 1997, and collected data from September 1997 until the end of mission in December 2010. SeaWiFS had 8 spectral bands from 412 to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. The mission and sensor were optimized for ocean color measurements, with a local noon (descending) equator crossing time orbit, fore-and-aft tilt capability, full dynamic range, and low polarization sensitivity.	proprietary
 Sea_of_Japan_0	Measurements from the Sea of Japan between 1999 and 2000	OB_DAAC STAC Catalog	1999-06-22		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360655-OB_DAAC.umm_json	Measurements from the Sea of Japan made between 1999 and 2000.	proprietary
@@ -13294,13 +13100,8 @@ TELLUS_GRAC_L3_GFZ_RL06_OCN_v04_RL06v04	GFZ TELLUS GRACE Level-3 Monthly Ocean B
 TELLUS_GRAC_L3_JPL_RL06_LND_v04_RL06v04	JPL TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 04	POCLOUD STAC Catalog	2002-04-04	2017-10-18	-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2077042612-POCLOUD.umm_json	The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
 TELLUS_GRAC_L3_JPL_RL06_OCN_v04_RL06v04	JPL TELLUS GRACE Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.0 version 04	POCLOUD STAC Catalog	2002-04-04	2017-10-25	-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2077042455-POCLOUD.umm_json	The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
 TELLUS_GRFO_L3_CSR_RL06.1_LND_v04_RL06.1v04	CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.1 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2537041932-POCLOUD.umm_json	The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
-TELLUS_GRFO_L3_CSR_RL06.1_OCN_v04_RL06.1v04	CSR TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.1 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2537034072-POCLOUD.umm_json	The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
-TELLUS_GRFO_L3_CSR_RL06.2_LND_v04_RL06.2v04	CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.2 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2767593027-POCLOUD.umm_json	The TELLUS_GRFO_L3_CSR_RL06.2_LND_v04 dataset produced by the the Center for Space Research (CSR) GRACE-FO program derives the terrestrial water storage anomaly using the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite data. GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. <br><br>    The RL06.2 v04 monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. <br><br>    Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.2 is an updated release version of the previous RL06.1. The difference is only in the handling of GPS data set during Level-2 processing. GPS handling for RL06.2 was re-assessed due to steady degradation in low degrees during late 2022 and 2023 in RL06.1. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in ASCII format have adopted the YAML encoding header, which is in full compliance with the PO.DAAC metadata best practices.    	proprietary
-TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04_RL06.2v04	CSR TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.2 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2767594383-POCLOUD.umm_json	This TELLUS_GRFO_L3_CSR_RL06.2_OCN_v04 dataset produced by the the Center for Space Research (CSR) GRACE-FO program derives the ocean bottom pressure (OBP) anomaly using the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite data. GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. <br><br>    The RL06.2 v04 monthly ocean mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. <br><br>    Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.2 is an updated release version of the previous RL06.1. The difference is only in the handling of GPS data set during Level-2 processing. GPS handling for RL06.2 was re-assessed due to steady degradation in low degrees during late 2022 and 2023 in RL06.1. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.    	proprietary
 TELLUS_GRFO_L3_CSR_RL06.3_LND_v04_RL06.3v04	CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C3193285193-POCLOUD.umm_json	This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br>  GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.	proprietary
 TELLUS_GRFO_L3_CSR_RL06.3_OCN_v04_RL06.3v04	CSR TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.3 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C3193289116-POCLOUD.umm_json	This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.  <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.	proprietary
-TELLUS_GRFO_L3_GFZ_RL06.1_LND_v04_RL06.1v04	GFZ TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.1 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2537039738-POCLOUD.umm_json	The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
-TELLUS_GRFO_L3_GFZ_RL06.1_OCN_v04_RL06.1v04	GFZ TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.1 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C2537032664-POCLOUD.umm_json	The monthly ocean bottom pressure anomaly grids are given as equivalent water thickness changes derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represent sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). The Level-2 GAD product has been added back, a glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters (i.e., de-striping and spatial smoothing) have been applied to reduce correlated errors. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. This RL06.1 is an updated release version of the previous RL06. The difference is only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; RL06.1 uses the ACH data product. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version and beyond, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.	proprietary
 TELLUS_GRFO_L3_GFZ_RL06.3_LND_v04_RL06.3v04	GFZ TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C3193293825-POCLOUD.umm_json	This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.  <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.	proprietary
 TELLUS_GRFO_L3_GFZ_RL06.3_OCN_v04_RL06.3v04	GFZ TELLUS GRACE-FO Level-3 Monthly Ocean Bottom Pressure Anomaly Release 6.3 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C3193298027-POCLOUD.umm_json	This data set is produced by the German Research Centre for Geosciences (GFZ) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the ocean bottom pressure (OBP) anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents sea floor pressure changes due to the integral effect of ocean and atmosphere processes, including global mean ocean bottom pressure changes (mean ocean mass and mean atmosphere mass over the global oceans). A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats.  <br><br> GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the ocean bottom pressure data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.	proprietary
 TELLUS_GRFO_L3_JPL_RL06.3_LND_v04_RL06.3v04	JPL TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04	POCLOUD STAC Catalog	2018-05-22		-180, -89.5, 180, 89.5	https://cmr.earthdata.nasa.gov/search/concepts/C3193302127-POCLOUD.umm_json	This data set is produced by the Jet Propulsion Laboratory (JPL) as part of the GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. <br><br>    GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this RL06.3 is an updated release of the previous RL06.1. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.	proprietary
@@ -15331,7 +15132,7 @@ VNP46A2_1	VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Glo
 VNP46A3_1	VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 15 arc-second Linear Lat Lon Grid	LAADS STAC Catalog	2012-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2062201748-LAADS.umm_json	VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 500m Linear Lat Lon Grid, with short-name VNP46A3, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A3 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This description pertains to the SNPP VIIRS Monthly Lunar BRDF-adjusted NTL collection, whose record starts from January 1st 2012.	proprietary
 VNP46A4_1	VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid	LAADS STAC Catalog	2012-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2062213246-LAADS.umm_json	VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This yearly Lunar BRDF-Adjusted NTL collection record starts from January 1st 2012.	proprietary
 VNP64A1_001	VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V001	LPDAAC_ECS STAC Catalog	2014-01-01	2019-01-02	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1632559364-LPDAAC_ECS.umm_json	The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.     VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/NPP_QA/NPPpage.cgi?fileName=maturity&subdir=forPage) data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.    The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.    Notification:  VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas.    	proprietary
-VNP64A1_002	VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V002	LPCLOUD STAC Catalog	2012-03-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2847928673-LPCLOUD.umm_json	The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.   VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask is scheduled for release in the summer of 2020.  The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format.  Notification:  VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas.  	proprietary
+VNP64A1_002	VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V002	LPCLOUD STAC Catalog	2012-03-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2847928673-LPCLOUD.umm_json	The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission.   The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. 	proprietary
 VOLPE_0	Chlorophyll-a measurements off the San Diego coast in 1999	OB_DAAC STAC Catalog	1999-09-14		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360696-OB_DAAC.umm_json	Measurements made off the San Diego, Californian coast in 1999.	proprietary
 VPRM_North_America_Parameters_1349_1	NACP VPRM NEE Parameters Optimized to North American Flux Tower Sites, 2000-2006	ORNL_CLOUD STAC Catalog	2000-01-01	2007-12-31	-156.63, 28.46, -68.74, 71.32	https://cmr.earthdata.nasa.gov/search/concepts/C2517710454-ORNL_CLOUD.umm_json	This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0). Observed eddy covariance data from the 65 tower sites, locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture were used as input data to optimize the VPRM parameters for the 65 sites. The data are provided by individual site, plant functional types (PFTs), and all sites together, and as monthly, annual, and all available data. The data are for the conterminous USA, Alaska, and Canada for the period 2000 to 2006.	proprietary
 VT_GOCE_Data_NA	VT GOCE Data	ESA STAC Catalog	2009-09-01	2012-07-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1965336957-ESA.umm_json	This collection contains the VT GOCE software and associated data set needed to run the software that is used for GOCE data visualisation.	proprietary
@@ -15446,8 +15247,6 @@ ZoblerSoilDerived_540_1	Global Soil Types, 0.5-Degree Grid (Modified Zobler)	ORN
 ZoblerSoil_418_1	Global Soil Types, 1-Degree Grid (Zobler)	ORNL_CLOUD STAC Catalog	1972-01-01	1982-01-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2216862716-ORNL_CLOUD.umm_json	A global digital data base of soil properties is available at 1 degree longitude resolution.  For each land cell, the data base includes major and associated soil units, surface texture, and slope; phase and miscellaneous land units are included where available. The data base was compiled as part of an effort to improve modeling of the hydrologic cycle in the GISS Genreal Circulation Model.	proprietary
 Zobler_Soil_649_1	SAFARI 2000 Soil Types, 0.5-Deg Grid (Modified Zobler)	ORNL_CLOUD STAC Catalog	1974-01-01	1981-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788353687-ORNL_CLOUD.umm_json	A SAFARI 2000 data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands.	proprietary
 ZonalFlux_0	Measurements from the western equatorial Pacific Ocean	OB_DAAC STAC Catalog	1996-04-17		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360710-OB_DAAC.umm_json	Measurements taken in the western equatorial Pacific Ocean in 1996.	proprietary
-a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0	An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.2071304, 47.5210264, 8.2382011, 47.543743	https://cmr.earthdata.nasa.gov/search/concepts/C3226081770-ENVIDAT.umm_json	Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.	proprietary
-a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0	A numerical solver for heat and mass transport in snow based on FEniCS	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8472494, 46.812044, 9.8472494, 46.812044	https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json	This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.     The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:    _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical  instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_    The code and potential updates can be accessed directly through git via:    https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics	proprietary
 a0d9764a3068439b997c42928ef739d2_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series  for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2	FEDEO STAC Catalog	1992-01-27	2010-06-13	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142504-FEDEO.umm_json	This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010.  It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage.  It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity.  The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH).	proprietary
 a1fdc436-0c81-43c4-93f0-b7b1abafe4da_NA	Resourcesat-2 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode	FEDEO STAC Catalog	2004-01-18		-25, 30, 45, 80	https://cmr.earthdata.nasa.gov/search/concepts/C2207458025-FEDEO.umm_json	Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale.	proprietary
 a386504aa8ae492f9f2af04c109346e9_NA	ESA Sea Level Climate Change Initiative (Sea_Level_cci): A database of coastal sea level anomalies and associated trends from Jason satellite altimetry from 2002 to 2018	FEDEO STAC Catalog	2002-01-15	2018-05-30	-30, -45, 160, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2548142874-FEDEO.umm_json	This dataset contains 17-year-long (June 2002 to May 2018 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of six regions: Mediterranean Sea, Northeast Atlantic, West Africa, North Indian Ocean, Southeast Asia and Australia. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from the ESA SL_cci+ v1.1 dataset of coastal sea level anomalies (also available in the catalogue, DOI:10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005), which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series. This large amount of coastal sea level estimates has been further analysed to produce the present dataset: it consists in a selection of 429 portions of satellite tracks crossing land for which valid sea level time series are provided at monthly interval together with the associated sea level trends over the 17-year time span at each along-track 20-Hz point, from 20 km offshore to the coast.The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset has a DOI: https://doi.org/10.17882/74354	proprietary
@@ -15465,8 +15264,6 @@ aae157df-5b91-4a49-b00b-d81729a566d7_NA	TerraSAR-X - High Resolution Spotlight I
 aae643e1a7614c24b6b604dea82cad93_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1	FEDEO STAC Catalog	2017-07-20	2017-08-20	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143151-FEDEO.umm_json	This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.	proprietary
 aamhcpex_1	AAMH CPEX	GHRC_DAAC STAC Catalog	2017-05-26	2017-07-16	154.716, 0.6408, -19.5629, 44.9689	https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json	The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format.	proprietary
 ab90030e26c54ba495b1cbec51e137e1_NA	ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31	FEDEO STAC Catalog	2002-07-24	2012-04-08	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142756-FEDEO.umm_json	The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31.   Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation.	proprietary
-above-and-below-ground-herbivore-communities-along-elevation_1.0	Above- and below-ground herbivore communities along elevation	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json	Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017.	proprietary
-accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0	Accessibility of the Swiss forest for economic wood extraction (2021)	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.umm_json	"Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport.  A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the ""helicopter"" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based.   Road transport was investigated using network analysis, based on the data set ""Forest access roads 2013"" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account.  Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the ""helicopter"" category or transport with trucks under 26 tons).   The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland.   The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php)  Input data used:   - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)"	proprietary
 accum-measurements-domec-traverse-1982_1	Accumulation Measurements from Pioneerskaya to Dome C, 1982-84	AU_AADC STAC Catalog	1982-01-01	1984-12-31	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json	Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 accumulation-movement-markers-mirny-domec_1	Detailed Notes on Accumulation/Movement Markers, Mirny-Dome C	AU_AADC STAC Catalog	1977-01-01	1978-12-31	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311711-AU_AADC.umm_json	Detailed notes about each of the markers used for movement (and accumulation) measurements along the Mirny-Dome C traverse line. Includes processing notes from the JMR position analysis.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 accumulation_lawdome_1960_1	Accumulation Measurements, Law Dome 1959-1960	AU_AADC STAC Catalog	1959-01-01	1960-12-31	110, -67, 115, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json	A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960.  These documents have been archived at the Australian Antarctic Division.	proprietary
@@ -15508,13 +15305,10 @@ aerial_photo_sea_ice_shapefiles_1	Flight lines and photo centres of aerial photo
 aerial_photographs_from_columbia_glacier_1976-2010_Not provided	Aerial Photographs from Columbia Glacier, 1976-2010	SCIOPS STAC Catalog	1976-07-24	2011-06-15	-146.895, 61.22, -146.895, 61.22	https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json	<p> Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010. </p> <p> This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year.  Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar). </p>	proprietary
 aerial_rpa_nov2016_1	Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016	AU_AADC STAC Catalog	2016-11-07	2016-11-20	77.9619, -68.5811, 78.0131, -68.5731	https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json	The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section.   The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs):  1 A report prepared by Doug Thost, the chief RPA pilot;  2 The aerial photographs of Davis and Heidemann Valley; and  3 Some panoramas created from aerial photographs taken at Davis.   The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections.   See the report for further details.	proprietary
 aerial_surveys_vestfold_2017-18_1	Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18	AU_AADC STAC Catalog	2017-11-19	2018-01-31	77.8923, -68.6067, 78.2235, -68.4809	https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json	"Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400  metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall.""  The spatial extents given in this metadata record are for the second survey.  For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and  (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level.  There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre.  Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM."	proprietary
-aerosol-data-davos-wolfgang_1.0	Aerosol Data Davos Wolfgang	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json	Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.    Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &#956;m) and GT-526S (0.3 – 5 &#956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&#8321;, PM&#8322;,&#8325; and PM&#8321;&#8320; (assumptions: particle density: 1.65 g cm&#713;&#179;, refractive index: 1.5+i0).    Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &#956;m and ~20 &#956;m by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&#713;&#179;).    Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&#713;&#179;).     Coriolis &#956; and DRINCZ: A microbial air sampler (Coriolis &#956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &#956;m were drawn with an air flow rate of up to 300 l min&#713;&#185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.	proprietary
-aerosol-data-weissfluhjoch_1.0	Aerosol Data Weissfluhjoch	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.806475, 46.832964, 9.806475, 46.832964	https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json	Aerosol properties were measured between February 8 and March 31 2019  at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.     Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 &#956;m) and GT-526S (0.3 – 5 &#956;m), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM&#8321;, PM&#8322;,&#8325; and PM&#8321;&#8320; (assumptions: particle density: 1.65 g cm&#713;&#179;, refractive index: 1.5+i0).     Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 &#956;m and ~20 &#956;m by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cm&#713;&#179;).     Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cm&#713;&#179;).     Coriolis &#956; and LINDA: A microbial air sampler (Coriolis &#956;, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 &#956;m were drawn with an air flow rate of up to 300 l min&#8254;&#185; into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.	proprietary
 aerosol_properties_725_1	SAFARI 2000 Physical and Chemical Properties of Aerosols, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-17	2000-09-13	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2789011485-ORNL_CLOUD.umm_json	SAFARI 2000 provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003).	proprietary
 aes5davg_236_1	BOREAS AES Five-day Averaged Surface Meteorological and Upper Air Data	ORNL_CLOUD STAC Catalog	1976-01-01	1997-01-01	-107.87, 52.17, -97.83, 57.35	https://cmr.earthdata.nasa.gov/search/concepts/C2807614663-ORNL_CLOUD.umm_json	Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba.	proprietary
 aes_upl1_238_1	BOREAS AFM-05 Level-1 Upper Air Network Data, R1	ORNL_CLOUD STAC Catalog	1993-08-16	1996-10-22	-111, 50.09, -93.5, 59.98	https://cmr.earthdata.nasa.gov/search/concepts/C2812433046-ORNL_CLOUD.umm_json	Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.	proprietary
 aes_upl2_239_1	BOREAS AFM-05 Level-2 Upper Air Network Standard Pressure Level Data	ORNL_CLOUD STAC Catalog	1993-08-16	1996-10-22	-111, 50.09, -93.5, 59.98	https://cmr.earthdata.nasa.gov/search/concepts/C2807616315-ORNL_CLOUD.umm_json	Basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.	proprietary
-afforestation-stillberg_1.0	Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.86716, 46.773573, 9.86716, 46.773573	https://cmr.earthdata.nasa.gov/search/concepts/C3226081740-ENVIDAT.umm_json	# Background information    The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Long-term monitoring of the large-scale high-elevation afforestation has generated data about tree growth, survival, and vitality. In addition, detailed characteristics of the microsite conditions of the research were conducted. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone.     # Experiment description    The Stillberg afforestation experiment was established in 1975 by planting 92,000 seedlings of *Larix decidua*, *Pinus cembra* and *Pinus mugo* ssp. *uncinata* in the alpine treeline ecotone. The afforestation site is located on a northeast-facing slope with steep, topographically highly structured terrain and covers elevations from 2075 to 2230 m a.s.l. The afforestation site was divided into 4052 square plots of 3.5 × 3.5 m, arranged in a regular species-alternating pattern over the whole area. Each plot contained 25 trees of one species (1350 plots per species), and the seedlings were systematically planted 70 cm apart. The trees have been monitored since 1975. Specifically, tree mortality was assessed annually from 1975 until 1995 and has been documented every ten years since then, with surveys in 2005 and 2015 (the next survey is due in 2025). Height of the surviving trees was measured in 1975, 1979, 1982, 1985, 1990, 1995, 2005, and 2015. In 1995, 2005, and 2015, drivers of tree vitality were assessed for a subset of trees per plot. Additionally, an extensive set of environmental parameters characterizing microsite conditions of the afforestation area were recorded before and after the planting of the trees.     # Data description    The five datasets from the afforestation experiment comprise ecological and environmental data from the main afforestation experiment in five datasets with accompanying metadata (Stillberg_afforestation_all_metadata.xlsx). All data and metadata files are bundled in a ZIP-file (Stillberg_afforestation_v1.zip). In particular, a first dataset contains environmental data characterising microsite conditions of the 4000 plots with regard to soil, topography, vegetation and microclimatic conditions (Stillberg_afforestation_plot_data_v1.csv; Stillberg_afforestation_plot_metadata_v1.csv. In each plot, the natural tree regeneration was assessed by counting seedings of several tree species in 2005 and 2015 (Stillberg_afforestation_regeneration_data_v1.csv; Stillberg_afforestation_regeneration_metadata_v1.csv). Furthermore, specific information about each of the 92’000 planted trees of the tree species is available (Stillberg_afforestation_tree_parameter_data_v1.csv; Stillberg_afforestation_tree_parameter_metadata_v1.csv). Survival data for each of the 92’000 individual trees can be found in a separate dataset (Stillberg_afforestation_tree_survival_data_v1.csv; Stillberg_afforestation_tree_survival_metadata_v1.csv). Tree growth and vitality parameters are available for all trees from 1995, and for subsets of trees for 2005 and 2015 (Stillberg_afforestation_tree_measurements_data_v1.csv; Stillberg_afforestation_tree_measurements_metadata_v1.csv).	proprietary
 afm06ihd_240_1	BOREAS AFM-06 Boundary Layer Height Data	ORNL_CLOUD STAC Catalog	1994-05-21	1994-09-20	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2807618371-ORNL_CLOUD.umm_json	Contains AFM-06 hourly inversion height measurements.	proprietary
 afm06ptd_241_1	BOREAS AFM-06 Mean Temperature Profile Data	ORNL_CLOUD STAC Catalog	1994-05-21	1994-09-21	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2807620192-ORNL_CLOUD.umm_json	Contains the AFM-06 temperature profiler data near the Old Jack Pine site in the Southern Study Area.	proprietary
 afm06pwd_242_1	BOREAS AFM-06 Mean Wind Profile Data	ORNL_CLOUD STAC Catalog	1994-05-21	1994-09-21	-104.4, 53.91, -104.4, 53.91	https://cmr.earthdata.nasa.gov/search/concepts/C2807620384-ORNL_CLOUD.umm_json	Contains the AFM-06 wind profiler data near the Old Jack Pine site in the Southern Study Area.	proprietary
@@ -15528,7 +15322,6 @@ afm4toas_498_1	BOREAS AFM-04 Twin Otter Aircraft Sounding Data	ORNL_CLOUD STAC C
 afm4tofx_497_1	BOREAS AFM-04 Twin Otter Aircraft Flux Data	ORNL_CLOUD STAC Catalog	1994-05-25	1994-09-19	-104.81, 53.79, -98.4, 55.95	https://cmr.earthdata.nasa.gov/search/concepts/C2808092173-ORNL_CLOUD.umm_json	Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction.	proprietary
 afm6gifs_433_1	BOREAS AFM-06 NOAA/ETL 35 GHz Cloud/Turbulence Radar GIF Images	ORNL_CLOUD STAC Catalog	1994-07-16	1994-08-08	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2928013317-ORNL_CLOUD.umm_json	The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994.	proprietary
 african_woody_savanna_850_1	Characteristics of African Savanna Biomes for Determining Woody Cover	ORNL_CLOUD STAC Catalog	1981-01-01	2003-12-31	-15.84, -27.75, 37.24, 16.76	https://cmr.earthdata.nasa.gov/search/concepts/C2784383572-ORNL_CLOUD.umm_json	This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005.  The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf).	proprietary
-agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0	Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json	"Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G.    Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions."	proprietary
 air_methane_lawdome_1	Dated Readings For Air Composition And Methane From Law Dome Ice Core	AU_AADC STAC Catalog	1988-01-01	1993-12-31	112.8, -66.771, 112.81, -66.77	https://cmr.earthdata.nasa.gov/search/concepts/C1214311761-AU_AADC.umm_json	"This work was completed as part of ASAC project 757 (ASAC_757).    This file comprises three main records compiled for publication in the following:   V. Morgan, M. Delmotte, T. van Ommen,  J. Jouzel, J. Chappellaz, S. Woon, V. Masson-Delmotte and D. Raynaud. Relative Timing of Deglacial Climate Events in Antarctica and Greenland, Science, 13 September 2002, Vol 297 (5588), pp. 1862-1864, DOI: 10.1126/science.1074257. Supporting Material - http://www.sciencemag.org/cgi/content/full/sci;297/5588/1862/DC1  Law Dome is a small (200 km in diameter) ice sheet located at the edge of the Indian Ocean sector of East Antarctica. The core site, near the summit of Law Dome (66 degrees 46'S, 112 degrees 48'E), is characterised by a high rate of accumulation (late Holocene average, 0.68 m ice equivalent per year) that results in an ice core with a highly tapered time scale in which the Holocene represents some 93% of the ice thickness of 1200 m. However, the full Law Dome isotopic record generally matches the long records from Vostok and Byrd to at least 80 ka, indicating that the record is continuous and undisturbed over this period. The Law Dome record is suited to gas-synchronisation studies because the high accumulation rate and consequent rapid burial give a small age difference (Delta age) between trapped air and the older enclosing ice.  Derivation of an age scale for the Law Dome core, is based upon a Dansgaard- Johnsen flow model (S1) matched to the observed layer thinning (S2). Continuously sampled seasonal cycles down to ~1/3 ice-thickness (~1ky) and spot measurements of seasonal layers to ~85% ice-thickness (~4 ky) constrain the ice-flow model through this period in which mean accumulation is assumed to be free of large trends. Chronological control in the lower portion of the ice-sheet prior to 4 ky is through ties to other records. For the period of discussion, namely 10 ky to 17 ky, ties at 9.6 ky, 11.0 ky, 11.6 ky, 12.5 ky, 12.8 ky, 14.3 ky and 16.3 ky, are obtained by matching air composition changes with those of GRIP. The 9.6 ky tie is obtained by matching to d18O of air in GRIP (S3) and GISP2 (S4) data, and the remainder synchronise with the Byrd and GRIP CH4 records (S5). Dust concentration data also provide additional constraints on the 16.3 ky tie. Beyond 16.3 ky control is by a tie at 32 ky (based on both dust and d18Oice matched to the Byrd ice core (S6) on the GRIP timescale (S5)). The mean temporal resolution of the LD isotope data is ~24y through this period.  The air-composition age-ties require Delta age computations for sequencing events within the LD record and for synchronisation of the chronology with GRIP. The high accumulation at DSS results in a particularly small Delta age value. The modern difference between ice-age and gas-age is 60 plus or minus 2 years for methane (S7). Note that at such low Delta age values, the diffusive mixing time from the free atmosphere down to seal-off depth becomes significant and must be accounted for; in the case of CH4 this is ~8 years (S7).  The absolute chronology derived for the LD record has contributions from both the LD and GRIP Delta age errors, but the relative timing between the LD CH4 and water isotope (d18Oice) signals is only uncertain to within the small errors associated with LD Delta age.  While the present-day trapping age at LD is small, lower temperatures and accumulation rates during the deglaciation lead to longer trapping times. To estimate Delta age under past conditions, we use a model (S8) to compute trapping age from accumulation and temperature (this model agrees with precise experimentally determined present day values). Since we have no direct indicators for palaeoaccumulation and palaeotemperature, we adopt two scenarios that use alternative estimation methods.  Estimation of palaeotemperature from the isotope data in both scenarios is by application of a calibration slope, ""Beta ppt/degrees C"". For the young chronology, which has minimum Delta age, the commonly applied spatial slope of Beta=0.67 ppt/degrees C is used, giving relatively warm temperatures. The default chronology uses a long-term temporal calibration (S9) for Law Dome, Beta=0.44 ppt/degrees C. This estimate, which is seasonally derived, gives greater temperature sensitivity for isotopic changes than the spatial slope. The use of this lower value for Beta is supported by direct comparisons between annual averages in d18O and temperature at the site and elsewhere on Law Dome. Over several years to a few decades, these yield coefficients of typically ~0.33 ppt/degrees C. We adopt the value 0.44 ppt/degrees C as a conservative choice, based on a longer-term calibration and because the incorporation of seasonal sea-ice variations may better capture glacial-to- Holocene environmental shifts.  Estimation of palaeoaccumulation for the young chronology is via the commonly applied method (see, e.g. S5) that scales modern accumulation-rate using the derivative of saturation vapour-pressure versus temperature relationship (also using Beta=0.67 ppt/degrees C). This method explicitly assumes no non-thermodynamic changes to moisture transport during climate variations (such as, e.g., atmospheric circulation changes) that may be important at this near-coastal location. Our alternative palaeoaccumulation estimate used for the default chronology assumes that the flow-model is correct and infers accumulation from the known age-intervals between the gas ties. This leads to considerably larger changes in accumulation which may nonetheless be understandable given the distinctively high Holocene precipitation regime that prevails at Law Dome. In addition, dust concentration data show a larger LGM to Holocene decrease at LD than Vostok. If relative flux changes at the two sites are similar, then the exaggerated dilution at LD is consistent with a large interglacial accumulation shift.  Trapped gas measurements were made in France: CH4 measurements at LGGE, Grenoble and d18Oair measurements at LSCE, Saclay. Both analyses were conducted using a wet extraction procedure to release the air of the ice and followed by an injection into a gas chromatograph (CH4 measurement) or by a mass spectrometer isotopic analysis (d18Oair measurements). Both analyses were conducted using established procedures (S10,S11). The methane analytical uncertainty is plus or minus 20 ppbv with values were obtained on a single measurement (in which the sample was exhausted) and are presented on the LGGE scale which differs slightly from the NOAA scale but is well calibrated against it: LGGE = 1.02*NOAA (S12). The d18Oair values arise from means of duplicate measurements (except for one point with an obvious experimental problem, 1127.492 m depth). The analytical precision for d18Oair is around 0.05 ppt with a mean reproducibility of about 0.1 ppt.  d18Oice measurements were made in Hobart and have an analytical precision of approximately 0.1 ppt. The results are expressed using the conventional reference of VSMOW (Vienna Standard Mean Ocean Water).  Supporting References and Notes S1. W. Dansgaard, S. J. Johnsen, J. Glaciol., 8, 215 (1969). S2. V. Morgan et al., J. Glaciol., 43, 3 (1997). S3. M. Cross, (Compiler) Greenland summit ice cores CD-ROM. Boulder, CO: National Snow and Ice Data Center in association with the World Data Center for Paleoclimatology at NOAA-NGDC, and the Institute of Arctic and Alpine Research (1997). S4. M. Bender et al., Nature 372, 663-666 (1994). S5. T. Blunier, et al., Nature 394, 739 (1998). S6. S. J. Johnsen, W. Dansgaard, H. B. Clausen, C. C. Langway, Nature, 235, 429 (1972). S7. D. M. Etheridge et al., J. Geophys. Res., 101, 4115 (1996). S8. J.-M. Barnola, P. Pimienta, D. Raynaud, Y. S. Korotkevich, Tellus Ser. B, 43, 83 (1991). S9. T. D. van Ommen, V. Morgan, J. Geophys. Res., 102, 9351 (1997). S10. J. Chappellaz, et al., J. Geophys. Res., 102, 15987, (1997). S11. B. Malaize, Analyse isotopique de l'oxygene de l'air piege dans les glaces de l'Antarctique et du Groenland: correlation inter-hemispheriques et effet Dole, PhD thesis, University Paris 6, (1998). S12. T. Sowers et al, J. Geophys. Res., 102, 26527, (1997)."	proprietary
 air_sea_gas_exchange_xdeg_1208_1	ISLSCP II Air-Sea Carbon Dioxide Gas Exchange	ORNL_CLOUD STAC Catalog	1995-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785340637-ORNL_CLOUD.umm_json	This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period.-Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000.-Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50  degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line. 	proprietary
 air_temperature_observations_in_the_arctic_1979-2004_Not provided	Air Temperature Observations in the Arctic 1979-2004	SCIOPS STAC Catalog	1979-01-01	2005-12-01	-180, 14.5, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json	The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004.	proprietary
@@ -15543,16 +15336,10 @@ alaskan_air_ground_snow_and_soil_temperatures__1998-2005_Not provided	Alaskan Ai
 albedo_line_snow_depths_Not provided	Albedo Line Snow Depths	SCIOPS STAC Catalog	2009-04-27	2009-04-28	-157, 71, -156, 72	https://cmr.earthdata.nasa.gov/search/concepts/C1214600343-SCIOPS.umm_json	Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed.  Data Citation:  Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2	proprietary
 ali_etm_tandem_821_1	SAFARI 2000 ALI/ETM+ Tandem Image Pair for Skukuza, South Africa, May 2001	ORNL_CLOUD STAC Catalog	2001-05-30	2001-05-30	30.76, -25.5, 33.12, -23.59	https://cmr.earthdata.nasa.gov/search/concepts/C2789740161-ORNL_CLOUD.umm_json	A tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite. ALI is a technology validation testbed that employs novel wide-angle optics and a highly integrated multispectral and panchromatic spectroradiometer.The tandem pair was produced to evaluate the differences between ALI and ETM+ and determine if technology similar to that of the ALI is suitable for future land imaging that will continue the observations begun by the Landsat satellites in 1972.The ALI and ETM+ images are false color composites combining shortwave infrared, near infrared, and visible wavelengths, displayed as red, green, and blue, respectively. Dense vegetation appears green. The similarity of the images demonstrates the ability of the ALI to produce data comparable to ETM+. Several SAFARI 2000 field campaigns conducted in KNP provided ground-based data needed to evaluate measurements from the satellite sensors.Each band is stored as an individual binary file. A metadata file accompanies each set of ALI and ETM+ band files to document the path and row number, sample and line counts, band file names, and sun azimuth and elevation angles. There is also a calibration parameter file that was used for 1R processing.	proprietary
 allADCP_GB_Not provided	Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC.	SCIOPS STAC Catalog	1995-04-25	1995-06-16	-68, 40.5, -67, 41.5	https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json	Acoustic Doppler Current Profiler (ADCP) observations, were             collected from the R/V Seward Johnson on two cruises to the Georges             Bank region, April-June 1995.  Three different ADCP units were used:             two broadband at 150 and 600 kHz, and one narrowband at 150 kHz.  The             broadband 150 kHz unit was used at anchor stations with data reported             at hourly intervals.  The broad-band 600 kHz and narrow-band 150 kHz             units collected data in the along track mode with data reported at             five minute intervals.  For each time interval, the u and v components             of currents are reported at uniform depth intervals throughout the             water column.               Ship                cruise        dates  R/V Seward Johnson  9506   1995 04 25 1995 05 02  R/V Seward Johnson  9508   1995 06 06 1995 06 16	proprietary
-alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0	Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4484863, 45.8115721, 9.4372559, 46.4586735	https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.umm_json	Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported.     Data used for the publication.  Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses.  Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees.	proprietary
 alos-prism-l1c_NA	ALOS PRISM L1C	ESA STAC Catalog	2006-08-01	2011-03-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2619280661-ESA.umm_json	"This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East.  The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: •	Time window: from 2006-08-01 to 2011-03-31 •	Orbits: from 2768 to 27604 •	Path (corresponds to JAXA track number): from 1 to 665 •	Row (corresponds to JAXA scene centre frame number): from 310 to 6790.  The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors).   The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width.  The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention."	proprietary
-alpine3d-simulations-of-future-climate-scenarios-for-graubunden_1.0	Alpine3D simulations of future climate scenarios for Graubunden	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.6737061, 46.2216525, 10.6347656, 47.1075228	https://cmr.earthdata.nasa.gov/search/concepts/C2789814545-ENVIDAT.umm_json	"This is the simulation dataset from _""Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland""_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources __55__, 4-16, 2013    A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.    The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.    Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation."	proprietary
-als-based-snow-depth_1.0	ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8683834, 46.829474, 9.8683834, 46.829474	https://cmr.earthdata.nasa.gov/search/concepts/C2789814552-ENVIDAT.umm_json	This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.   All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.     __Paper Citation:__   > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216,  [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._	proprietary
 amanda_bay_sat_1	Amanda Bay Satellite Image Map 1:100 000	AU_AADC STAC Catalog	1991-12-01	1991-12-31	75, -70, 78, -69	https://cmr.earthdata.nasa.gov/search/concepts/C1214311750-AU_AADC.umm_json	Satellite image map of Amanda Bay, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:100 000, and was produced from Landsat 4 TM imagery (124-108, 124-109). It is projected on a Transverse Mercator projection, and shows traverses/routes/foot track charts, glaciers/ice shelves, penguin colonies, stations/bases, runways/helipads, and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
 amazon_precip_228_1	Amazon River Basin Precipitation, 1972-1992	ORNL_CLOUD STAC Catalog	1972-01-01	1992-12-31	-79.6, -20, -49.4, 5.2	https://cmr.earthdata.nasa.gov/search/concepts/C2776924642-ORNL_CLOUD.umm_json	The precipitation data is 0.2 degree gridded monthly precipitation data based upon monthly rain data from Peru and Bolivia and daily rain data from Brazil. The extent of the data ranges from 5.2N and -20.0S to -49.4W to -79.6W	proprietary
 ames_sunphotometer_643_1	SAFARI 2000 Airborne Sunphotometer Aerosol Optical Depth and Water Vapor Data	ORNL_CLOUD STAC Catalog	2000-08-14	2000-09-16	11, -26, 36, -14	https://cmr.earthdata.nasa.gov/search/concepts/C2788351532-ORNL_CLOUD.umm_json	The NASA Ames Airborne Tracking 14-channel Sunphotometer (AATS-14) was operated successfully aboard the University of Washington CV-580 for 24 data flights during the dry-season airborne campaign from August 13 to September 25, 2000. Flights originated from Pietersburg, South Africa; Kasane, Botswana; and Walvis Bay, Namibia. The AATS-14 instrument measures the transmission of the direct solar beam at 14 discrete wavelengths (350-1558 nm) from which we derived spectral aerosol optical depths (AOD) and columnar water vapor (CWV).	proprietary
-amount_of_dead_wood-214_1.0	Amount of dead wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814565-ENVIDAT.umm_json	Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs  starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-amphibian-and-landscape-data-swiss-lowlands_1.0	Amphibian and urban-rural landscape data Swiss Lowlands	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.7124023, 47.0776041, 9.0637207, 47.7983967	https://cmr.earthdata.nasa.gov/search/concepts/C2789814582-ENVIDAT.umm_json	"The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban ""green"" and ""grey"" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity."	proprietary
-amphibian-data-aargau_1.0	Amphibian observation and pond data (Aargau, Switzerland)	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	7.7, 47.15, 8.46, 47.62	https://cmr.earthdata.nasa.gov/search/concepts/C2789814599-ENVIDAT.umm_json	In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each.  The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.  The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.	proprietary
 amprimpacts_1	Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-18	2023-03-02	-124.153, 26.507, -64.366, 49.31	https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json	The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format.  	proprietary
 amprtbcp_2	AMPR BRIGHTNESS TEMPERATURE CAPE EXPERIMENT	GHRC_DAAC STAC Catalog	1991-07-21	1991-08-16	-83.2024, 0, 12.6618, 38.1879	https://cmr.earthdata.nasa.gov/search/concepts/C1977858384-GHRC_DAAC.umm_json	The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation/Electrification Experiment (CaPE). AMPR data werecollected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of July 21, 1991 - Aug. 16, 1991. CaPE took place in centralFlorida between 43 N - 25.5 N latitude and 86 W - 69 W longitude.	proprietary
 amprtbcx1_2	AMPR BRIGHTNESS TEMPERATURE CAMEX-1	GHRC_DAAC STAC Catalog	1993-09-26	1993-10-05	-83.8511, 23.9917, -68.2377, 42.6325	https://cmr.earthdata.nasa.gov/search/concepts/C1977858400-GHRC_DAAC.umm_json	The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1) conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25.5N - 43N latitude and 70W - 83W longitude.	proprietary
@@ -15571,41 +15358,28 @@ ams_cs96_406_1	BOREAS/AES Campbell Scientific 15-minute Surface Meteorological D
 amsua15sp_1	ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15	GHRC_DAAC STAC Catalog	1998-08-03		-180, -90, 180, 89.756	https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json	AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit.	proprietary
 amsua16sp_1	ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16	GHRC_DAAC STAC Catalog	2001-05-27	2009-07-30	-180, -89.91, 180, 89.73	https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json	AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit.	proprietary
 amsua17sp_1	ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17	GHRC_DAAC STAC Catalog	2002-07-21	2003-12-13	-180, -89.575, 180, 89.629	https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json	AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster.	proprietary
-anezet-analysing-net-zero-transformations_1.0	ANEZET: Analysing Net-Zero Transformations	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.umm_json	We have analysed past transformations in Switzerland in four environmental domains, with the aim to  draw conclusions for current challenges, such as the net‐zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research.	proprietary
-angle-of-repose-of-snow_1.0	Angle of repose experiments with natural and spherical snow	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814625-ENVIDAT.umm_json	Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to  examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.     This dataset consists of  1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.    The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.	proprietary
 ant_dist_1	Antarctic Distances	AU_AADC STAC Catalog	1996-11-01	1996-11-01	45, -90, 160, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214311734-AU_AADC.umm_json	Spreadsheet of distances between Antarctic locations (eg. Mawson Station, Prince Edward Island) and world locations (eg. Melbourne, Santiago).	proprietary
 ant_seafloor_geomorph_1	Antarctic wide seafloor geomorphology	AU_AADC STAC Catalog	2009-07-21	2009-07-21	-180, -77, 180, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214305709-AU_AADC.umm_json	Publicly available bathymetry and geophysical data can be used to map geomorphic features of the Antarctic continental margin and adjoining ocean basins at scales of 1:1-5 million. These data can also be used to map likely locations for some Vulnerable Marine Ecosystems. Seamounts over a certain size are readily identified and submarine canyons and mid ocean ridge central valleys which harbour hydrothermal vents can be located. Geomorphic features and their properties can be related to major habitat characteristics such as sea floor type (hard versus soft), ice keel scouring, sediment deposition or erosion and current regimes. Where more detailed data are available, shelf geomorphology can be shown to provide a guide to the distribution in the area of the shelf benthic communities recognised by Gutt (2007). The geomorphic mapping method presented here provides a layer to add to benthic bioregionalistion using readily available data.  An AADC maintained copy of these data are publicly available for download from the provided URL.  The master copy of these data are attached to the metadata record held at Geoscience Australia (see the provided URL).	proprietary
 antarctic_biodiversity_db_1	Antarctic Biodiversity Database	AU_AADC STAC Catalog	1995-09-30		-180, -90, 180, -53.1	https://cmr.earthdata.nasa.gov/search/concepts/C1214311754-AU_AADC.umm_json	The biodiversity database is planned to be a reference on Antarctic and subantarctic flora and fauna collated by the Regional Sensitivity to Climate Change (RiSCC) group and developed by the Australian Antarctic Data Centre.  Searches are available in the following areas:  Taxonomy Protection and convention measures (protected species) Observations Scientific Bibliographies	proprietary
 antarctic_circumpolar_current_fronts_1	Fronts of the Antarctic Circumpolar Current - GIS data	AU_AADC STAC Catalog	1970-01-01		-180, -71, 180, -30	https://cmr.earthdata.nasa.gov/search/concepts/C1613496025-AU_AADC.umm_json	This line shapefile represents the following features of the Antarctic Circumpolar Current: Subtropical Front (STF); Subantarctic Front (SAF); Southern Antarctic Circumpolar Current Front (sACCf); Polar Front (PF); Southern Boundary of the Antarctic Circumpolar Current   as described in   Alejandro H. Orsi, Thomas Whitworth III, and Worth D. Nowlin Jr (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current.  Deep-Sea Research 42 (5), 641-673.  The shapefile was created from data provided by lead author Alejandro Orsi to the Australian Antarctic Data Centre in August 2001. The data in the files from Alejandro Orsi was also combined in a csv file.    The data available for download includes the original data, the shapefile and the csv file.	proprietary
 antarctic_single_frames_Not provided	USGS Antarctic Single Frame Records	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567558-USGS_LTA.umm_json	Antarctic Single Frame Records are a collection of aerial photographs over Antarctica from the United States Antarctic Resource Center (USARC) and the British Antarctic Survey (BAS) dating from 1946 to 2000. The Antarctic Single Frame Records collection includes black-and-white, natural color and color infrared images with a photographic scale ranging from 1:1,000 to 1:64,000. 	proprietary
-anthropogenic-change-and-net-n-mineralization_1.0	Anthropogenic change and soil net N mineralization	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	158.90625, -54.9776137, -132.1875, 61.2702328	https://cmr.earthdata.nasa.gov/search/concepts/C2789814650-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology    Please cite this paper together with the citation for the datafile.    We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).	proprietary
 aoci0bil_281_1	BOREAS Level-0 AOCI Imagery: Digital Counts in BIL Format	ORNL_CLOUD STAC Catalog	1994-07-21	1994-07-21	-105.91, 52.98, -104.93, 54.46	https://cmr.earthdata.nasa.gov/search/concepts/C2927616228-ORNL_CLOUD.umm_json	The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. 	proprietary
 apr3cpex_1	Airborne Precipitation Radar 3rd Generation (APR-3) CPEX	GHRC_DAAC STAC Catalog	2017-05-27	2017-06-24	-96.0262, 16.8091, -69.2994, 28.9042	https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json	The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format.	proprietary
 apr3cpexaw_1	Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW	GHRC_DAAC STAC Catalog	2021-08-20	2021-09-04	-80.7804, 11.8615, -45.6417, 34.046	https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json	The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format.	proprietary
 apr3cpexcv_1	Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV	GHRC_DAAC STAC Catalog	2022-09-02	2022-09-30	-89.6733315, 1.7593585, -14.8189435, 39.1985524	https://cmr.earthdata.nasa.gov/search/concepts/C2708951073-GHRC_DAAC.umm_json	The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross-section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through September 30, 2022, in netCDF-4 format, with associated browse imagery in JPG format.   	proprietary
 apuimpacts_1	Autonomous Parsivel Unit (APU) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-15	2020-02-29	-75.5894, 37.919, -75.3588, 38.2064	https://cmr.earthdata.nasa.gov/search/concepts/C1995564696-GHRC_DAAC.umm_json	The Autonomous Parsivel Unit (APU) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format from January 15, 2020 through February 29, 2020.	proprietary
-area_of_shrub_forest-123_1.0	Area of shrub forest	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814712-ENVIDAT.umm_json	All plots classified as shrub forest according to the NFI forest definition.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-arthropod-biomass-abundance-species-richness-trends-limpach_1.0	Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	7.3819542, 47.0815787, 7.528553, 47.1334543	https://cmr.earthdata.nasa.gov/search/concepts/C2789814758-ENVIDAT.umm_json	Recent publications about declines in arthropod biomass, abundance and species diversity  raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types  were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58’255 specimens of 1’343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics.    Data are provided on request to contact person against bilateral agreement.	proprietary
 asas_Not provided	Advanced Solid-state Array Spectroradiometer (ASAS)	USGS_LTA STAC Catalog	1988-06-26		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.umm_json	The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle.  The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.).  Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994.	proprietary
 asas_l1b_562_1	BOREAS RSS-02 Level-1b ASAS Image Data: At-sensor Radiance in BSQ Format	ORNL_CLOUD STAC Catalog	1994-04-19	1996-07-20	-106.32, 53.24, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2813527156-ORNL_CLOUD.umm_json	The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude.  The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996.	proprietary
 asasrefl_287_1	BOREAS RSS-02 Extracted Reflectance Factors Derived from ASAS Imagery	ORNL_CLOUD STAC Catalog	1994-05-24	1996-07-20	-106.2, 53.24, -104.62, 53.99	https://cmr.earthdata.nasa.gov/search/concepts/C2813382300-ORNL_CLOUD.umm_json	Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS.	proprietary
 ascatcpex_1	Advanced Scatterometer (ASCAT) CPEX	GHRC_DAAC STAC Catalog	2017-05-24	2017-07-16	160.241, 3.9062, -25.0958, 42.5176	https://cmr.earthdata.nasa.gov/search/concepts/C2428509185-GHRC_DAAC.umm_json	The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.	proprietary
 asosimpacts_1	Automated Surface Observing System (ASOS) IMPACTS	GHRC_DAAC STAC Catalog	2019-12-29	2023-03-01	-89.694, 36.571, -67.791, 47.467	https://cmr.earthdata.nasa.gov/search/concepts/C1995871063-GHRC_DAAC.umm_json	The Automated Surface Observing Systems (ASOS) IMPACTS dataset consists of a variety of ground-based observations during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This ASOS dataset consists of 176 stations within the IMPACTS domain. Each station provides observations of surface temperature, dew point, precipitation, wind direction, wind speed, wind gust, sea level pressure, and the observed weather code. The ASOS data are available from December 29, 2019, through March 1, 2023, in netCDF-4 format.	proprietary
 aspas_asmas_aat_3	Antarctic Specially Protected Areas and Antarctic Specially Managed Areas in the Australian Antarctic Territory - GIS polygon dataset.	AU_AADC STAC Catalog	1998-01-01	2008-01-01	60.867, -72.967, 142.7, -66.217	https://cmr.earthdata.nasa.gov/search/concepts/C1457769795-AU_AADC.umm_json	This record describes GIS polygon data (a shapefile) representing the boundaries of Antarctic Specially Protected Areas (ASPAs) and an Antarctic Specially Managed Area (ASMA) in the Australian Antarctic Territory for which Australia was the proponent or co-proponent. Also included is the boundary of ASPA 168 for which China was the proponent.  The following is a list of the ASPAs and ASMA: ASPA 101 Taylor Rookery ASPA 102 Rookery Islands ASPA 103 Ardery Island and Odbert Island ASPA 135 North-east Bailey Peninsula ASPA 136 Clark Peninsula ASPA 143 Marine Plain ASPA 160 Frazier Islands ASPA 162 Mawson's Huts ASPA 164 Scullin and Murray Monoliths ASPA 167 Hawker Island ASPA 168 Mt Harding ASPA 169 Amanda Bay ASPA 174 Stornes ASMA 6 Larsemann Hills  The data is available from a link in this metadata record and also, as a separate shapefile for each ASPA or ASMA, from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database (see related url). GIS data representing the boundaries of other ASPAs and ASMAs is also available from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database.	proprietary
-asrb-dav_1.0	ASRB_DAV: Shortwave and longwave radiation measurements (2 min) in Davos Dorf	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.84827, 46.81277, 9.84827, 46.81277	https://cmr.earthdata.nasa.gov/search/concepts/C2789814851-ENVIDAT.umm_json	Incoming and outgoing  shortwave and longwave 2 min radiation measurements in Davos Dorf, CH.     ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.	proprietary
-asrb-vf_1.0	ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.809204, 46.829631, 9.809204, 46.829631	https://cmr.earthdata.nasa.gov/search/concepts/C2789814947-ENVIDAT.umm_json	Incoming and outgoing  shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.    ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.	proprietary
-asrb-wfj_1.0	ASRB_WFJ: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.809204, 46.829631, 9.809204, 46.829631	https://cmr.earthdata.nasa.gov/search/concepts/C2789814987-ENVIDAT.umm_json	Corrected incoming and outgoing  shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH.     ### References  1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.  2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.	proprietary
 aster_1	Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre	AU_AADC STAC Catalog	2000-10-08		-180, -90, 180, -53	https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json	Advanced Spaceborne Thermal Emission and Reflection Radiometer.  Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling.  There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data.   Search the Satellite Image Catalogue for more information using the link included.	proprietary
 aster_global_dem_Not provided	ASTER Global DEM	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567908-USGS_LTA.umm_json	ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras.  Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m).   The methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive.  Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels.  All cloud-screened DEMS are stacked and residual bad values and outliers are removed.  Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (°) x 1° tiles.  The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles.  Tiles that contain at least 0.01% land area are included.  The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude).  The data are posted on a  1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid.	proprietary
 atlas_buildings_gis_1	Differential GPS survey of the Atlas Cove ANARE Station ruins on Heard Island	AU_AADC STAC Catalog	2000-01-01	2000-02-28	73.3, -53.1, 73.5, -53	https://cmr.earthdata.nasa.gov/search/concepts/C1214313143-AU_AADC.umm_json	Alistair Grinbergs (Heritage Officer) was on Heard island in January and February 2000) as part of the 2000 ANARE, to make an assessment of the heritage value of the old ANARE station ruins.  This GPS survey data of the corners of buildings and other artefacts will form part of the record of the station site, together with drawings and other measurements.  The assessment will be used to formulate a conservation management plan for the site.	proprietary
 atlas_cove_photos_1	Atlas Cove Terrestrial Photos - historic ANARE Base	AU_AADC STAC Catalog	2008-03-26	2008-03-26	73.391, -53.02, 73.394, -53.018	https://cmr.earthdata.nasa.gov/search/concepts/C1214313131-AU_AADC.umm_json	Photographs and photo locations of the historic Australian National Antarctic Research Expedition (ANARE) base at Atlas Cove on Heard Island.  The station was established 11 December 1947 and was closed down on 9 March 1955.  Photos were taken in March of 2008 by Kerry Steinberner during a visit to Heard Island.  The map used to locate the images is described in the following metadata record:  Topographic Survey at Atlas Cove, Heard Island, November 2000 [atlas_survey2000_gis]  The images include shots of the remains of ANARE buildings, vehicles, tanks, debris, fences, artefacts and flora.  The dataset includes a copy of the images, an excel spreadsheet cataloguing the images, and shapefiles showing the image locations.	proprietary
 atlas_photocontrol_gis_1	Differential GPS survey of points at Atlas Cove for control of 1987 aerial photography	AU_AADC STAC Catalog	2000-01-01	2000-02-28	73.3, -53.1, 73.5, -53	https://cmr.earthdata.nasa.gov/search/concepts/C1214313132-AU_AADC.umm_json	Dave Gardner was on Heard Island in January and February 2000 as part of the 2000 ANARE.  Opportunistic use was made of the the differential gps system to take accurate locations of 16 points identified from the 1987 aerial photography, so that they could be used as reference points for merging the photographs into an accurate photo mosaic.  Around the station and to the NE it was easy to identify features from the photographs with confidence.  To the west of the station the topography and features of the azorella wallows had changed significant and it was not possible to identify features with confidence.	proprietary
-atlfishref-a-12s-mitochondrial-reference-dataset-for-metabarcoding-atlantic-fish_1.0	ATLFISHREF A 12S mitochondrial reference dataset for metabarcoding Atlantic Fishes frequently caught during scientific surveys in the Bay of Biscay	ENVIDAT STAC Catalog	2024-01-01	2024-01-01	-5.6469727, 43.0158943, -0.9008789, 48.3810645	https://cmr.earthdata.nasa.gov/search/concepts/C3226081750-ENVIDAT.umm_json	The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade.  Thanks to a large spatio-temporal coverage,  high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the “Teleo” barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature).  This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.	proprietary
 atmos_co2_by_erosion_xdeg_1019_1	ISLSCP II Atmospheric Carbon Dioxide Consumption by Continental Erosion	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785296327-ORNL_CLOUD.umm_json	The Continental Atmospheric CO2 Consumption data set represents gridded estimates for the riverine export of carbon and of sediments based on empirical models. All data exist for the overall continental area in a spatial resolution of 0.5 x 0.5 degree longitude/ latitude. The units are tC/km2/yr for all carbon species, and t/km2/yr for sediment fluxes. There are two data files (*.zip) with this data set which describe the following: dissolved organic carbon (DOC) export, particulate organic carbon (POC) export, bicarbonate export, export of bicarbonate being of atmospheric origin (also called atmospheric CO2 consumption by rock weathering), and sediment export.	proprietary
-atree-forest-owner-clearances-offsetting_1.0	ATREE forest owners survey about forest clearances offsetting in the forest	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.0175171, 46.7689338, 7.800293, 47.1027556	https://cmr.earthdata.nasa.gov/search/concepts/C2789815028-ENVIDAT.umm_json	In April 2020, about 1700 forest owners of the plateau region of the Canton of Berne were invited to participate in a survey (virtually all of them received a conventional paper-pencil questionnaire) about their willingness to provide forest nature conservation measures in their forest to compensate forest clearances that cannot be compensated by afforestation. The questionnaire contained a survey experiment (conjoint analysis) that offered a choice between two options and the status quo in 9 decision-making situations. Of the 607 completed questionnaires that were returned the survey experiment was completed by about 400.	proprietary
-atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0	ATREE forest owners survey about climate regulation services of forests	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814546-ENVIDAT.umm_json	Forest owners of the Canton of Lucerne were survey about their willingness to employ different forest management measures to provicde climate regulation services by forests. Of the nearly 3000 forest owners that received an invitation to a online-survey and the 900 forest owners that received a paper and pencil survey, 1055 valid responses were received. The questionnaire contained a survey experiment in which 9 choice situations were presented to the respondents in which they had the choice between two options and the status quo. This survey experiment part of the survey was completed by 990 respondents.	proprietary
-atree-q-methodology-forest-clearances-offsetting_1.0	ATREE Q-methodology statement sorts on forest clearances offsetting in the forest	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814556-ENVIDAT.umm_json	"In Novdember 2019 about 19 experts on forest surface protection and forest clearances were invited to a workshop in order to discuss policy design and implementation problems regarding the offsetting of forest clearances. In Switzerland such offsetting can be provided under certain circumstances by implementing forest nature conservation measures in the forest instead of providing in-kind compensation, i.e. reafforestation on agricultural land. The workshop included the sorting of 34 statements – that were elaborated beforehand, partially also with help of the participants – according to the ""Q-methodology"" survey technique (participants arrange given statements about a certain subject into boxes that are normally distributed over a ""agree - do not agree"" answer scale). The participants included representatives from cantonal and national forest administrations, nature conservation NGOs, forest NGOs, spatial planning NGOs, private counseling enterprises as well as national, cantonal and regional forest owner organizations. The data allows a factor analytical differentiation of actors into groups with distinct positions towards forest clearance compensation as well as a positioning of these groups relative to each statement."	proprietary
-atree-social-network-analysis-carbon-sequestration-lucerne_1.0	ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.0859375, 46.9348859, 8.470459, 47.2191951	https://cmr.earthdata.nasa.gov/search/concepts/C2789814569-ENVIDAT.umm_json	"In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the ""actor coalition framework"" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations."	proprietary
 atrs_Not provided	Airborne Coherant Radar Sounding Data	SCIOPS STAC Catalog	1970-01-01		-180, -90, 180, -70	https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json	"Developmental airborne coherent radar sounding data collected over a variety of       sounding targets in Antarctica, including a full gridded survey of subglacial       Lake Vostok and its environs.  This was an instrument development award, so the       data are not of ""production"" quality.  Receiver sensitivity documents are       provided with the data.              The data resides in 6, DLT 4 tapes (~30 Gb each)."	proprietary
 au0103_1	Aurora Australis marine science cruise au0103 (CLIVAR_SR3) - CTD and ADCP data	AU_AADC STAC Catalog	2001-10-29	2002-12-13	139, -68, 148, -43	https://cmr.earthdata.nasa.gov/search/concepts/C1214306658-AU_AADC.umm_json	Oceanographic measurements were conducted along CLIVAR Southern Ocean meridional repeat transect SR3 between Tasmania and Antarctica from October to December 2001. A total of 135 CTD vertical profile stations were taken, more than half to within 20 m of the bottom. Over 2200 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, CFC's, CCl4, dissolved inorganic carbon, alkalinity, 13C, DMS/DMSP/DMSO, halocarbons, barium, barite, ammonia, del30Si, dissolved and particulate organic carbon, particulate silica, 15N-nitrate, 18O, 234Th, 230Th, 231Pa, primary productivity and biological parameters, using a 24 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Two sediment trap moorings were serviced, and a third mooring was deployed at a new location. A summary of all CTD data and data quality is presented in the data report.  This work was completed as part of ASAC project 1335.	proprietary
 au0106_1	Aurora Australis Southern Ocean oceanographic data, voyage 6, 2000-2001 - KACTAS	AU_AADC STAC Catalog	2001-01-01	2001-03-09	61.875, -68.26939, 148.11719, -43.61071	https://cmr.earthdata.nasa.gov/search/concepts/C1709216539-AU_AADC.umm_json	Oceanographic measurements conducted on voyage 6 of the Aurora Australis of the 2000-2001 season.  These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data.  These data were collected by Mark Rosenberg.  This metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise.  Basic information about voyage 6: The voyage will complete a range of Marine Science activities off the Mawson Coast, and off the Amery Ice Shelf before calling at Davis to retrieve summer personnel and helicopters prior to returning to Hobart. Science equipment calibration will be undertaken at Mawson. (Marine Science activities were interrupted when the Aurora Australis was required to provide assistance in the Polar Bird's attempt to reach Casey, complete the station resupply and return to open water.)  Leader: Dr Graham Hosie Deputy Leader: Mr Andrew McEldowney  See the readme files in the downloads for more information.	proprietary
@@ -15634,13 +15408,6 @@ au97_9806_1	Aurora Australis SAZ Southern Ocean oceanographic data, cruise au97_
 au97_9807_1	Aurora Australis SAZ Southern Ocean oceanographic data, cruise au97_9807	AU_AADC STAC Catalog	1998-03-04	1998-05-22	77, -68, 159, -41	https://cmr.earthdata.nasa.gov/search/concepts/C1667367792-AU_AADC.umm_json	Oceanographic measurements were conducted on a cruise of the Aurora Australis to the Southern Ocean in April and May of 1998. A total of 97 CTD vertical profiles were taken. Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate), dissolved inorganic carbon, alkalinity, carbon isotopes, dissolved organic carbon, N2O isotopes, pH, oxygen-18, barium, nitrogen-15, arsenic, ammonia, DMS/P, bacteria, silicon-32, particulate silicon, productivity, ETS, pigments, species counts, cytometry, particulate organic carbon and nitrogen, urea, copper and iron, using a 24 bottle rosette sampler.      These data have been recovered by the AADC - as such this is a generic metadata record.  The fields in this dataset are: oceanography ship station number date start time bottom time finish time cruise start position bottom position finish position maximum position bottom depth pressure temperature (T-90) salinity sigma-T specific volume anomaly geopotential anomaly dissolved oxygen fluorescence photosynthetically active radiation	proprietary
 au9901_1	Aurora Australis Southern Ocean oceanographic data, voyage 1, 1999-2000 - IDIOTS	AU_AADC STAC Catalog	1999-07-13	1999-09-07	142.38281, -69.77895, 160.66406, -43.58039	https://cmr.earthdata.nasa.gov/search/concepts/C1709216544-AU_AADC.umm_json	Oceanographic measurements conducted on voyage 1 of the Aurora Australis of the 1999-2000 season.  These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data.  These data were collected by Mark Rosenberg.  This metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise.  Basic information about voyage 1: Polynya study off Mertz Glacier at about 145 deg E. The vessel departed from Port Arthur for the polynya study site without returning to Hobart. The voyage also deployed moorings and delivered biologists (for seal and penguin programs) and a small quantity of essential supplies and mail to Macquarie Island.  Leader: Dr Ian Allison Deputy Leader: Dr Tony Worby Cargo Supervisor: Dr Vicky Lytle  See the readme files in the downloads for more information.	proprietary
 auslig_m7_Not provided	Critical Aeronautical Heights for Australia, 30 deg. by 30 deg., Australian Survey and Land Information Group (AUSLIG)	CEOS_EXTRA STAC Catalog	1995-01-01	1995-12-31	110, -45, 155, -10	https://cmr.earthdata.nasa.gov/search/concepts/C2231957520-CEOS_EXTRA.umm_json	"The M7 data represent the highest points in each 30 minute by 30       minute grid square for Australia.              see: 'http://www.ga.gov.au/'              The following text was abstracted from Bruce Gittings' Digital       Elevation Data Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'.       The catalogue is a comprehensive source of information on digital       elevation data and should be retrieved in its entirety for additional       information.              Australian Data is available from the Australian Survey and Land       Information Group (AUSLIG).  There are three products; M7 are Critical       Aeronautical Heights which represent the highest point in each 30'x30'       quad, M8 are Spot heights (ie. an irregular grid) and M9 represents an       18"" (~500m) grid at 1:250,000 scale (gridded from M8 using an       Hutchinson Algorithm).  Both M8 and M9 have incomplete coverage of the       country.              The 500m grid covers 30% of Australia (Southern New South Wales,       Victoria, parts of Northern Queensland and selected cities).  The size       of the 1:250 000 scale files is 60,501 points each x 63 files =       38,176,131 elevation points.  Costs: License 1:250 000 AU$1000 / File       1:100 000 AU$250.              NB. 1996: 100m and 200m DEMs covering all of Australia are also       now available.  Prices are as follows (in US Dollars):              Per km2      Total Cost  Copyright Restrictions       -------      ----------  ----------------------       100m DEM    $0.0028      $21,433     One-time license fee       200m DEM    $0.0017      $13,089     ""  ""              URL: 'http://www.auslig.gov.au/'"	proprietary
-automated-avalanche-release-area-pra-delineation-davos_1.0	Automated Avalanche Release Area (PRA) Delineation Davos	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.7503662, 46.7125608, 9.8876953, 46.8517391	https://cmr.earthdata.nasa.gov/search/concepts/C2789814581-ENVIDAT.umm_json	"This dataset contains the output and reference data published in the paper ""Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping"" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.    Abstract:  Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.    In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available."	proprietary
-automatic-classification-of-avalanches_1.0	Automatic Classification of Avalanches	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.9223037, 46.7200638, 9.9223037, 46.7200638	https://cmr.earthdata.nasa.gov/search/concepts/C2789814596-ENVIDAT.umm_json	This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.	proprietary
-avalanche-accidents-in-switzerland-since-1970-71_1.0	Avalanche accidents in Switzerland since 1970/71	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081213-ENVIDAT.umm_json	**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.    This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one person involved (caught). The data set commences on 01/10/1970. After the completion of a hydrological year, the new data is added.    The following information is provided:     * avalanche identifier      * date of the accident     * accuracy of the date in range of days before and after    * canton     * municipality     * start zone point latitude   * start zone point longitude   * start zone point accuracy (in meters)   * start zone point elevation (in meteres above sea level)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)       * forecasted avalanche danger level 1 (first danger)     * forecasted avalanche danger level 2 (second danger)   * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)    * number of dead persons      * number of caught persons     * number of fully buried persons     * activity/location of the accident party at the time of the incident	proprietary
-avalanche-fatalities-european-alps-1969-2015_1.0	Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	4.5703125, 43.0367759, 16.5673828, 48.4292006	https://cmr.earthdata.nasa.gov/search/concepts/C2789814622-ENVIDAT.umm_json	"During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.    Data complementing Figure 2 in:    _""Avalanche fatalities in the European Alps: long-term trends and statistics""_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.    Data description:    please refer to section 2 (Data and Methods) in the mentioned publication"	proprietary
-avalanche-fatalities-per-calendar-year-since-1936_1.0	Number of avalanche fatalities per calendar year in Switzerland since 1937	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814645-ENVIDAT.umm_json	Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which  is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset. If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]  The following information is contained (by column and column title):    - year    - number of fatalities in the backcountry (=tour)    - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)    - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)    - number of fatalities in or around buildings or in settlements (= buildings)    - sum (of all four categories)  The definitions for these four categories, as described in the guidelines to the avalanche accident database are:  __tour:__   activities include back-country ski, snowboard or snow-shoe touring    __offpiste:__   access from ski area, generally from the top of a skilift with short hiking distances    __transportation.corridors__ (Techel et al., 2016):   people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)    __buildings__ (Techel et al., 2016):   people inside or just outside buildings, and workers on high alpine building sites	proprietary
-avalanche-fatalities-switzerland-1936_1.0	Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814658-ENVIDAT.umm_json	Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per  hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.   The following information is contained (by column and column title):    - hydrological year    - number of fatalities in the backcountry (=tour)    - number of fatalities in terrain close to ski areas (=offpiste)    - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)    - number of fatalities in or around buildings or in settlements (= buildings)    - sum (of all four categories)    The definition for these four categories as described in the guidelines to the avalanche accident database:         **tour**:      activities include back-country ski, snowboard or snow-shoe touring    **offpiste**:   access from ski area, generally from the top of a skilift with short hiking distances    **transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):    people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)     **buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):   people inside or just outside buildings, and workers on high alpine building sites	proprietary
-avalanche-prediction-snowpack-simulations_1.0	Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081494-ENVIDAT.umm_json	The data set contained in this repository was used in the analysis by Mayer et al. (2023):  Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.	proprietary
 avapsimpacts_1	Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-12	2023-02-28	-77.815, 33.54, -65.44, 44.17	https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json	The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format.	proprietary
 avhrr_822_1	SAFARI 2000 AVHRR Daily Site (1.5 km) and 15-Day Regional (1.5- and 6-km) Imagery	ORNL_CLOUD STAC Catalog	1998-07-01	2000-10-31	8.73, -35.26, 43.2, -7.49	https://cmr.earthdata.nasa.gov/search/concepts/C2804805089-ORNL_CLOUD.umm_json	The Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images. These AVHRR data contain four main sets of data:1.5 km daily site extracts of SAFARI core sites (2000)1.5 km 15-day composites of SAFARI core sites (1998-2000)1.5 km 15-day composites of the southern African region (Mar, Sept 2000)6 km 15-day composites of the southern African region (1998-2000)The primary data layers for site extracts and regional composites are fire pixel counts and maximum NDVI. The fire product is different for the daily and for the composited products (see readme file) and a fire product is not included in the 1.5 km regional data set.  NDVI composite-associated data layers for the regional data sets include land surface temperature, reflectance, solar zenith angle, view zenith angle, and relative azimuth angle. NDVI composite-associated data layers for the site extracts include these same variables as well as brightness temperature, fire mask composite, latitude, and longitude. The data are stored in binary image format files. There is a metadata file for each site and date/compositing period, in ASCII format.	proprietary
 avhrr_albedo_1995_xdeg_928_1	ISLSCP II AVHRR Albedo and BRDF, 1995	ORNL_CLOUD STAC Catalog	1995-02-01	1995-07-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784840966-ORNL_CLOUD.umm_json	This Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree. Black-sky albedo (direct beam contribution) and white-sky (Completely diffuse contribution) can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon. 	proprietary
@@ -15659,25 +15426,16 @@ b431fbecf73c4442ad5d7bcf80929b03_NA	ESA Ozone Climate Change Initiative (Ozone C
 b480d7c8-3694-4772-8294-941f3d3ede9f_1	European remote sensing forest/non-forest digital map	CEOS_EXTRA STAC Catalog	1992-01-01	1993-09-28	-12, 38, 44, 74	https://cmr.earthdata.nasa.gov/search/concepts/C2232847861-CEOS_EXTRA.umm_json	"The European Remote Sensing Forest/Non-forest Digital Map was originally prepared for the European Space Agency (ESA) as a contribution to the World Forest Watch project of the International Space Year (ISY), 1992. The actual production of the map was carried out by a consortium of four companies, GAF mbH (Munich FRG), the Swedish Space Corporation (Kiruna), SCOT Conseil (France) and the National Land Survey of Finland (Helsinki). It is based entirely on the digital classification of NOAA/AVHRR-HRPT* one-kilometer resolution multispectral data, approximately 70 scenes from the summer periods only of 1990 to 1992.  As such, the European Forest/Non-forest Digital Map is reasonably up-to- date and based on a homogeneous data source.  Because the methodology used to produce the digital map is documented and was ""economically"" accomplished, the product is presumably replicable and could therefore be updated and/or used for monitoring purposes at scales of up to 1:2 million (ESA/ESTEC, 1992).  The following steps are a summary of those actually used by the consortium in the production of the digital map:    -  Satellite data selection (minimal cloud cover)/acquisition;   -  Data pre-processing for a) geometric correction and b) cloud masking;   -  Data subset stratification into homogeneous spectral zones;   -  Data subset classification (Bayesian maximum likelihood);   -  Accuracy assessment (using classified Landsat MSS);   -  Mosaicking of classified data subsets;   -  Merging of final results and overlays;   -  Cartographic preparation.  The producers of the digital map used only data from AVHRR channels 1, 2 and 3 with ""maximal geometric and radiometric resolution""; that is, the central 1200 to 1600 pixels of any given scan line, to map European forest areas greater than one square kilometer.  Because the AVHRR sensor is not capable of distinguishing among different European forest types, many broad classes (Boreal, Central European and Mediterranean) are grouped together as ""forest"" in the digital map.  * - the National Oceanic and Atmospheric Administration (NOAA) /     satellite's Advanced Very High Resolution Radiometer (AVHRR)     sensor - and High Resolution Picture Transmission (HRPT) data.   For the 32 Landsat scenes compared with the NOAA/AVHRR forest/non-forest classification, the overall accuracy (percentage of pixels ""correctly"" classified) was calculated as 82.5%, and the surface area accuracy (degree of agreement in areal extent between the NOAA/AVHRR results and the Landsat MSS used as ""ground truth"") was found to be 93.8%.  Format of the Original ESA/ESTEC-Provided Data Set  The European Forest/Non-Forest Digital Map was provided to GRID on a single 150-Mb data cartridge, as a total of seven ARC/INFO-format data files for separate parts of the continent as follows:  Northwest; North; Central; Southwest and Southeast Europe; the Commonwealth of Independent States (CIS, up to the Ural Mountains only); and North Africa.  A total of 53 countries are included altogether.  Within this original digital map, data are coded by country and category (i.e. forest, non-forest or water), but ""overall"" selections of one category or another are rendered difficult because the codes are in combination (i.e. country + category). Also, the large size of the seven individual ARC/INFO coverages all but prohibits working with the digital data for the entire pan-European area.  Explanation of the Data Processing done by GRID  GRID's objective in data processing of the European Forest/Non-forest Digital Map was to create a single seamless product covering most of the continent, for forestry and GIS studies at a pan-European level. The assemblage of the seven original individual coverages prepared for ESA/ESTEC into a single entity proved impractical due to both hardware and software limitations; thus, the seventh and largest portion for the Commonwealth of Independent States (CIS) was left out of the overall assemblage.  Even so, it was still necessary to generalize the data somewhat, given the total number of polygons (>100000) and arcs (>170000) in the remaining six original coverages.  Thus, the following methodology was followed to reduce the amount of data and assemble the six coverages into a single product (all data processing was done using commands in the ARC/INFO software):  -  Polygon elimination based on area - After several experiments, polygons with an area smaller than four square kilometers (sq. km.) were eliminated.  This minimum area proved to be a good compromise between original forest patterns and number of polygons eliminated (total of 70%).  The equivalent of four sq. km. at a central latitude within each of the six original coverages was calculated, and this value was used in the 'ELIMINATE' command.  It would have been more accurate to perform the 'ELIMINATEs' with the data in an equal-area projection, but for practical reasons (space and time) they were not.  -  Assembling six coverages into one - The six coverages were put together using the 'MAPJOIN' command.  The software limitation of a maximum 10000 arcs per polygon was circumvented by splitting the outer polygon of Europe into three separate parts.   -  Editing errors produced by step (2) - The 'MAPJOIN' command puts adjacent coverages together and recreates topology using an assigned distance known as the ""fuzzy tolerance"" factor.  Any reasonable factor forces some lines to converge, creating dangling arcs and new polygons without IDs.  As a result, interactive editing of the new coverage was necessary to delete dangling arcs, and to assign proper polygon IDs.   -  Update of the topology - After the modifications made in step (3), it was necessary to re-create the polygon topology using 'CLEAN'.  -  Addition of INFO item 'classes' - A new numeric item (format 3 3 I) was added in the polygon attribute table (.PAT) to contain the following values: 1) Forest; 2) Non-forest; and 3) Water.  This item allows a user to select e.g. all of the European forested area polygons, as opposed to just those within a single country, in one simple INFO command.  The European Forest/Non-forest data set is available from GRID as one ARC/INFO 'EXPORT'-format data file in the Geographic Projection, which covers an area from 20 to 80 degrees North latitude, and -30 degrees West to 60 degrees East longitude.  The single data file ""EURO_FOR.E00"" comprises 77.25 Mb., but after being 'IMPORTed' to the equivalent ARC/INFO coverage, is reduced to 19.7 Mb in size.  There is also the separate, original (non-generalized) data file which covers the CIS area alone; this additional 'EXPORT'-format data file ""CIS.E00"" comprises 68.262 Mb.  Users who would prefer to have other original portions of the European Forest/Non-forest Digital Map listed above, as opposed to the GRID version documented herein, are requested to contact ESA/ESTEC at the address listed below.  Reference and Source  The source of the data set is the ESA/ESTEC ISY Office*, as modified by UNEP/GRID-Geneva.  The proper reference to the data set is ""ESA, 1992, Remote sensing forest map of Europe (brochure), ESA/ESTEC, 18 pages."" ESA/ESTEC also provides a paper entitled ""Digital data set of the remote sensing forest map of Europe; guidelines for data handling (as prepared by GAF-Munich in April 1993)"", which contains much useful information about their original digital data product and the seven individual data files they distribute as one entity. In addition, ESA/ESTEC distributes a paper map of the original product having the same name as above, at a scale of 1:6 000 000 (the paper map uses the Lambert Azimuthal Equal-Area projection).   * - the European Space Agency/European Space Research and Technology     Centre - the International Space Year; P. O. Box 299; 2200 AG     Noordwijk; The Netherlands (Mr. K. Pseiner; fax = 01719-17400). "	proprietary
 b64b1a0ad7874fb39791e99c57b944bc_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 3.1	FEDEO STAC Catalog	1997-09-03	2016-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142812-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 3.1 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites).  Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).	proprietary
 b673f41b-d934-49e4-af6b-44bbdf164367_NA	AVHRR - Land Surface Temperature (LST) - Europe, Daytime	FEDEO STAC Catalog	1998-02-23		-24, 28, 57, 78	https://cmr.earthdata.nasa.gov/search/concepts/C2207458008-FEDEO.umm_json	"The ""Land Surface Temperature derived from NOAA-AVHRR data (LST_AVHRR)"" is a fixed grid map (in stereographic projection ) with a spatial resolution of 1.1 km. The total size covering Europe is 4100 samples by 4300 lines. Within 24 hours of acquiring data from the satellite, day-time and night-time LSTs are calculated. In general, the products utilise data from all six of the passes that the satellite makes over Europe in each 24 hour period. For the daily day-time LST maps, the compositing criterion for the three day-time passes is maximum NDVI value and for daily night-time LST maps, the criterion is the maximum night-time LST value of the three night-time passes. Weekly and monthly day-time or night-time LST composite products are also produced by averaging daily day-time or daily night-time LST values, respectively. The range of LST values is scaled between –39.5°C and +87°C with a radiometric resolution of 0.5°C. A value of –40°C is used for water. Clouds are masked out as bad values. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/"	proprietary
-bark-and-wood-boring-insects-in-pines_1.0	Infestation of Scots pines with different vitalities by bark and wood boring insects	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.5627136, 46.2249145, 7.8984833, 46.3184179	https://cmr.earthdata.nasa.gov/search/concepts/C2789814729-ENVIDAT.umm_json	After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack.	proprietary
 baro-levelling-to-domec_1	Barometric Leveling Results, Pioneerskaya to Dome C	AU_AADC STAC Catalog	1955-01-01	1985-12-31	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313156-AU_AADC.umm_json	Record of barometric leveling measurements taken during the traverse from Pioneerskaya to Dome C (year currently unknown).  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 baro_pressure_1968_1	Barometric Pressure and Air Temperature Measurements, Law Dome and Wilkes Land, 1968	AU_AADC STAC Catalog	1968-01-01	1968-12-31	110, -68, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214306681-AU_AADC.umm_json	Measurements taken of barometric pressure and air temperature during traverse across Law Dome and Wilkes Land in 1968.  Copies of these documents have been archived in the records store of the Australian Antarctic Division.	proprietary
-basal_area-92_1.0	Basal area	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814819-ENVIDAT.umm_json	Sum of the stem cross-section areas of all living trees and shrubs  starting at 12 cm dbh (standing and lying) at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-basal_area_of_dead_wood-171_1.0	Basal area of dead wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814926-ENVIDAT.umm_json	Sum of the stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-basal_area_of_dead_wood_nfi1-247_1.0	Basal area of dead wood NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814964-ENVIDAT.umm_json	Sum of stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height) recorded according to the NFI1 method.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-base-cation-dynamics-in-an-oriental-beech-forest_1.0	Base cation dynamics in an Oriental beech forest	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	54.216156, 36.4371199, 54.2518616, 36.4570042	https://cmr.earthdata.nasa.gov/search/concepts/C2789815006-ENVIDAT.umm_json	Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.  In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.	proprietary
 basin_border_670_1	LBA Regional Boundary for the Amazon and Tocantins River Basins, 5-min	ORNL_CLOUD STAC Catalog	1972-01-01	1972-12-31	-85, -30, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2776926615-ORNL_CLOUD.umm_json	This data set is an expanded version of the Costa et al. (2000) data set and consists of a single grid with values of 1 for cells within the basins and 0 for cells outside. The resolution of the data set is 5 x 5 min (approximately 9 x 9 km). The area of this data set is consistent with the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data file is in ASCII GRID format.	proprietary
 bathy_proposedMPAs_eastantarctica_1	Bathymetry Compilation for Proposed Marine Protected Areas in East Antarctica	AU_AADC STAC Catalog	1979-10-19	2010-12-02	32, -72.5, 150, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214313157-AU_AADC.umm_json	The Australian Antarctic Division (AAD) has developed a proposal for the establishment of seven Marine Protected Areas (MPAs) located around east Antarctica for the purposes of marine ecosystem conservation. As seafloor morphology is a key component of marine ecosystems, this bathymetry compilation for the proposed MPAs was produced to support the AAD proposal. All bathymetry data available to Geoscience Australia at the time of compilation were used. This included multibeam and singlebeam acoustic data which were verified and processed to ensure the data were as accurate as possible. Processing included sound velocity corrections, navigation verification and the rejection of erroneous data points. Once processed, the data were gridded to 100m resolution and projected into suitable WGS84 UTM zones. The gridded data was exported into several formats to facilitate ease of use. The formats include xyz files, ESRI rasters, geoTIFs, CARISTM image files and soundings.  The data and the technical report are available for download from URLs below.	proprietary
-bats-and-nocturnal-insects-in-urban-green-areas_1.0	Bats and nocturnal insects in urban green areas	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	1.8237305, 47.2195681, 8.8110352, 51.5360856	https://cmr.earthdata.nasa.gov/search/concepts/C2789814542-ENVIDAT.umm_json	Animal biodiversity in cities is generally expected to be uniformly reduced, but recent studies show that this is modulated by the composition and configuration of Urban Green Areas (UGAs). UGAs represent a heterogeneous network of vegetated spaces in urban settings that have repeatedly shown to support a significant part of native diurnal animal biodiversity. However, nocturnal taxa have so far been understudied, constraining our understanding of the role of UGAs on maintaining ecological connectivity and enhancing overall biodiversity. We present a well-replicated multi-city study on the factors driving bat and nocturnal insect biodiversity in three European cities. To achieve this, we sampled bats with ultrasound recorders and flying insects with light traps during the summer of 2018. Results showed a greater abundance and diversity of bats and nocturnal insects in the city of Zurich, followed by Antwerp and Paris. We identified artificial lighting in the UGA to lower bat diversity by probably filtering out light-sensitive species. We also found a negative correlation between both bat activity and diversity and insect abundance, suggesting a top-down control. An in-depth analysis of the Zurich data revealed divergent responses of the nocturnal fauna to landscape variables, while pointing out a bottom-up control of insect diversity on bats. Thus, to effectively preserve biodiversity in urban environments, UGAs management decisions should take into account the combined ecological needs of bats and nocturnal insects and consider the specific spatial topology of UGAs in each city.	proprietary
 bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA	TerraSAR-X - Staring Spotlight Images (TerraSAR-X Staring Spotlight)	FEDEO STAC Catalog	2007-06-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2207458066-FEDEO.umm_json	"This collection contains radar image products of the German national TerraSAR-X mission acquired in Staring Spotlight mode. Staring Spotlight imaging allows for a spatial resolution of up to 25 cm. The scene size varies depending on the incidence angle. As an example, 4 km (across swath) x 3.7 km (in orbit direction) can be achieved at 60°. TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.			For more information concerning the TerraSAR-X mission, the reader is referred to:			https://www.dlr.de/content/de/missionen/terrasar-x.html"	proprietary
 bds_dragonfly_Not provided	A Checklist of British and Irish Dragonfly Species	SCIOPS STAC Catalog	1998-01-01		-8.41, 49.49, 2.39, 59.07	https://cmr.earthdata.nasa.gov/search/concepts/C1214611738-SCIOPS.umm_json	"Dragonflies are among the most ancient of living creatures. Fossil records, clearly recognisable as dragonflies, go back to Carboniferous times which means that they date back almost 300 million years, predating pterodactyls by 100 million years and birds by some 150 million. It would he tragic if, after surviving such an unimaginable number of years, it should be our generation that witnesses the decline of these fascinating and beautiful insects.  The British Dragonfly Society maintains a checklist of British and Irish dragonflies. This checklist includes all British and Irish species including migrants, vagrants and species now believed extinct in the British Isles. The species name provides a link to a photograph where available.  Information was obtained from ""http://www.british-dragonflies.org.uk/content/uk-species""."	proprietary
 beaver_sat_1	Beaver Lake Satellite Image and Topographic Double-sided Map 1:100 000	AU_AADC STAC Catalog	1990-05-01	1990-05-31	67, -71, 69, -70	https://cmr.earthdata.nasa.gov/search/concepts/C1214313272-AU_AADC.umm_json	Double-sided satellite image and topographic map of Beaver Lake, Antarctica. These maps were produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990.  Both maps are at a scale of 1:100 000. The satellite image map was produced from SPOT 1 and LANDSAT 5 TM scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases and gives some historical text information. The map has both geographical and UTM co-ordinates. Contours on the topographic map were derived from Russian maps (values have not been verified.) This map is also projected on a transverse mercator projection, and shows traverses/routes/foot track charts, bases/stations, glaciers/ice shelves, survey marks, and gives some historical text information.	proprietary
 bech_nest_locations_1	Adelie Penguin nest locations on Bechervaise Island	AU_AADC STAC Catalog	2000-02-01	2000-02-22	62.8084, -67.5879, 62.8152, -67.5863	https://cmr.earthdata.nasa.gov/search/concepts/C1214313158-AU_AADC.umm_json	This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour.  The dataset contains three files - an image file and two zip files.  The image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations.  The zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island.  The zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations.	proprietary
-beech_stress_thresholds_1.0	Stress thresholds of mature European beech trees	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	6.5368652, 45.9799133, 9.7009277, 47.6044342	https://cmr.earthdata.nasa.gov/search/concepts/C2789814551-ENVIDAT.umm_json	This data set contains the data presented in the figures 1-6 in Walthert et al. (2020): From the comfort zone to crown dieback: sequence of physiological stress thresholds in mature European beech trees across progressive drought. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2020.141792.  A detailed methodical description of the data can be found in the Material and Methods section of the paper.    Drought responses of mature trees are still poorly understood making it difficult to predict species distributions under a warmer climate. Using mature European beech (Fagus sylvatica L.), a widespread and economically important tree species in Europe, we aimed at developing an empirical stress-level scheme to describe its physiological response to drought. We analysed effects of decreasing soil and leaf water potential on soil water uptake, stem radius, native embolism, early defoliation and crown dieback with comprehensive measurements from overall nine hydrologically distinct beech stands across Switzerland, including records from the exceptional 2018 drought and the 2019/2020 post-drought period. Based on the observed responses to decreasing water potential we derived the following five stress levels: I (predawn leaf water potential >-0.4 MPa): no detectable hydraulic limitations; II (-0.4 to -1.3): persistent stem shrinkage begins and growth ceases; III (-1.3 to -2.1): onset of native embolism and defoliation; IV (-2.1 to -2.8): onset of crown dieback; V (<-2.8): transpiration ceases and crown dieback is >20%. Our scheme provides, for the first time, quantitative thresholds regarding the physiological downregulation of mature European beech trees under drought and therefore synthesises relevant and fundamental information for process-based species distribution models. Moreover, our study revealed that European beech is drought vulnerable, because it still transpires considerably at high levels of embolism and because defoliation occurs rather as a result of embolism than preventing embolism. During the 2018 drought, an exposure to the stress levels III-V of only one month was long enough to trigger substantial crown dieback in beech trees on shallow soils. On deep soils with a high water holding capacity, in contrast, water reserves in deep soil layers prevented drought stress in beech trees. This emphasises the importance to include local data on soil water availability when predicting the future distribution of European beech.	proprietary
-bender2020_1.0	Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020)	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.7568359, 45.7828484, 10.7336426, 48	https://cmr.earthdata.nasa.gov/search/concepts/C2789814563-ENVIDAT.umm_json	This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.    This includes:     * meteorological forcing,   * climate change timeries and   * simulation files together with   * snow depth  * ground temperature     __Please refer to the following publication for further details which should be cited when using this dataset:__    __Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.__	proprietary
 beryllium_10be_isotopes_lawdome_1	High resolution studies of cosmogenic beryllium isotopes (10Be) at Law Dome	AU_AADC STAC Catalog	2013-03-01	2013-03-31	112.80535, -66.7059, 112.80534, -66.7058	https://cmr.earthdata.nasa.gov/search/concepts/C1214571598-AU_AADC.umm_json	"Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output.  3 x 3"" PICO firn cores were drilled immediately in front of snow pit.  The 3 pico cores were sampled at 14cm intervals and the sections combined resulting in 16 samples. Some length was lost during transit, especially in the top cores. It was assumed that the lost length was from the breaks in the core as the ends rubbed against each other during transport, and was evenly lost from each break, using the field notes to help. The bottom of each core was assumed to be the lengths as measured in the field.  The samples were placed in a melting jar with carrier and left to melt overnight. ~10mL of the samples were retained for water isotope analysis. The samples were filtered and pumped onto cation columns."	proprietary
 beryllium_7be_isotopes_lawdome_1	High resolution studies of cosmogenic beryllium isotopes (7Be) at Law Dome	AU_AADC STAC Catalog	2013-03-01	2013-03-31	112.80535, -66.7059, 112.80534, -66.7058	https://cmr.earthdata.nasa.gov/search/concepts/C1214571593-AU_AADC.umm_json	Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output.  A ~1.4 x 1 x 1 m pit was dug on Law Dome. The wall was flattened using a ~60 cm level, handsaw and paint scrappers. A significant sastrugi could be seen in the top right of the wall. Sampling was started on the left of the wall to avoid this where possible. Wearing plastic gloves to avoid contaminating the samples, the top surface was levelled to the lowest point, and some of the snow collected as sample P1-1. It was around 4 cm at its highest point. A 10 cm x 10 cm grid was drawn into the wall, covering 80 cm x 80 cm. The top 10 cm layer was sawn out of the wall using a hand saw, cutting into the wall by at least 20 cm along the horizontal 10 cm below the top surface, then the back 20 cm from the front surface, and finally chopping the large block into smaller blocks.  The extra six blocks were discarded, and the two samples were put into zip lock bags as P2-1 and P2-2. The back of the sampling area was cleared back to allow easier access for the next layer. This was repeated for seven more layers, finishing with P9.  One block from each level was used for density measurements. The samples from each level were combined into a melting jar and carrier added. For some samples, not all the blocks fitted at once, so a portion of the blocks were melted (with the carrier) in the oven at 60 degrees C. The samples were allowed melt completely overnight. ~10mL of the samples were retained for water isotopes . The samples were filtered though 41 microns and the 0.45 microns and pumped onto cation columns.	proprietary
-bet_1.0	Bryophytes of Europe Traits (BET) dataset	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	-31.1718714, 26.214591, 70.1953197, 82.3206462	https://cmr.earthdata.nasa.gov/search/concepts/C3226081833-ENVIDAT.umm_json	The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).	proprietary
 bf471155-d77b-47d2-a3d4-22ea5f291fb6_NA	MERIS - Water Parameters - Baltic Sea, Monthly	FEDEO STAC Catalog	2006-01-01	2012-04-08	6.98888, 52.1246, 34.1429, 66.7187	https://cmr.earthdata.nasa.gov/search/concepts/C2207458063-FEDEO.umm_json	The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra.The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.	proprietary
 bf5eae2a052848aab2abf93d96e7e9aa_NA	ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ensemble product), Version 2.6	FEDEO STAC Catalog	1995-08-01	2002-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143216-FEDEO.umm_json	In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite.The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily, monthly and yearly aerosol products from the ATSR-2 instrument on the ERS-2 satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 1995 to 2002.    In 2002, it also contains data from the AATSR instrument on the ENVISAT satellite. A separate AATSR product covering the period 2002-2012 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation.	proprietary
 bf8dbf94-ff16-42bf-a957-0e8f80813aff_NA	METOP GOME-2 - Nitrogen Dioxide (NO2) - Global	FEDEO STAC Catalog	2007-01-23		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2207458015-FEDEO.umm_json	"The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.			The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products.			The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method.			For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/"	proprietary
@@ -15688,25 +15446,12 @@ bhd_inclinometer_temp_1977_1	Inclinometer and Temperature Readings From Ice Core
 bhq_ice_core_logbooks_1	Logbooks from the drilling of ice cores at site BHQ, Law Dome 1977	AU_AADC STAC Catalog	1977-01-01	1977-12-31	110, -67, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214306685-AU_AADC.umm_json	A collection of 3 books recording the details of the ice cores drilled at site BHQ on Law Dome in 1977. Includes limited stratigraphy information on some core segments.  A hard copy of this document has been archived in the Australian Antarctic Division records section.	proprietary
 bhq_temp_1977_1	BHQ Temperature and Drill Time, Law Dome 1977	AU_AADC STAC Catalog	1977-01-01	1977-12-31	110, -67, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214306700-AU_AADC.umm_json	Results for the temperatures recorded from the BHQ ice core borehole in 1977.  A hard copy of this document has been archived in the Australian Antarctic Division records section.	proprietary
 billmark_828_1	SAFARI 2000 Aerosol Fatty Acid and Stable Isotope Data, Mongu, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-17	2000-09-06	23.1, -15.15, 23.1, -15.15	https://cmr.earthdata.nasa.gov/search/concepts/C2779734738-ORNL_CLOUD.umm_json	The Southern African Regional Science Initiative (SAFARI 2000) was conducted in part to investigate the impacts of the large-scale transport and deposition of increasingly anthropogenic emissions on southern African biogeochemical cycling. Aerosol samples from the Mongu site in eastern Zambia were collected and analyzed to identify chemical biomarkers during the SAFARI 2000 dry season field campaign. Total suspended particulate aerosol samples were collected diurnally for a period of two weeks during August and September of 2000.These data include bulk organic carbon, nitrogen and sulfur stable isotopic measurements of total suspended particulate aerosols and gas chromatography/mass spectrometry (GC/MS) analysis of fatty acids extracted from collected aerosols. These data were used to chemically describe temporal variability in aerosol compositions.	proprietary
-bioclim_plus_1.0	CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789814586-ENVIDAT.umm_json	A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.	proprietary
-biodiversity-integration_1.0	"Replication files for ""Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics"""	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814605-ENVIDAT.umm_json	"## Introduction    The ZIP file contains all data and code to replicate the analyses reported in the following paper.    Reber, U., Fischer, M., Ingold, K., Kienast, F., Hersperger, A. M., Grütter, R., & Benz, R. (2022). Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics. *Policy Sciences*. [https://doi.org/10.1007/s11077-022-09456-4](https://doi.org/10.1007/s11077-022-09456-4)    If you use any of the material included in this repository, please refer to the paper. If you use (parts of) the text corpus, please also refer to the sources used for its compilation listed below. The content of the texts may not be changed.    ## Data folder    The data folder contains the following files.    * _corpus.parquet_: Text corpus of Swiss policy documents  * _dict_de.csv_: Biodiversity dictionary (German)  * _dict_fr.csv_: Biodiversity dictionary (French)  * _dict_it.csv_: Biodiversity dictionary (Italian)  * _topic_labels.csv_: labels/codes for policy sectors  * _topics.csv_: labels/codes for policy sectors    The corpus and the dictionary were compiled by the authors specifically for this project. The labels/codes for policy sectors are based on the [coding scheme](http://ws-old.parlament.ch/affairs/topics) of the Swiss Parliament.      ### Text corpus    The text corpus consists of 439,984 Swiss policy documents in German, French, and Italian from 1999 to 2018. The corpus was compiled from the following source between 2020-10-01 and 2021-01-31.    * Transcripts and parliamentary businesses (e.g. questions, motions, parliamentary initiatives) via the [Web Services (WS)](https://www.parlament.ch/de/%C3%BCber-das-parlament/fakten-und-zahlen/open-data-web-services) provided by the Swiss Parliament  * The official compilation of federal legislation (""Amtliche Sammlung"", AS) via [opendata.swiss](https://opendata.swiss/de/dataset/official-compilation-of-federal-legislation-bs-as-1947-2018) provided by the Swiss Federal Archives (SFA)  *  The federal gazette (""Bundesblatt"") via [fedlex.admin.ch](https://www.fedlex.admin.ch/de/fga/index)  * Decisions of federal courts via [entscheidsuche.ch (ES)](https://entscheidsuche.ch/)    The corpus is stored in a single data frame to use with R saved as [PARQUET](https://parquet.apache.org/) file (corpus.parquet). The data frame has the following structure.    * _text_id_: Unique identifier for each text (source information as prefix, e.g. ""t_"")  * _doc_type_: Document type (see coding scheme below)  * _branch_: Government branche (1 legislative, 2 executive, 3 judicative)  * _stage_: Stage of policy process (1 drafting, 2 introduction, 3 interpretation)  * _year_: Year of publication  * _topic_: Policy sector (coding scheme in separate file in data folder)  * _lang_: Language (de, fr, it)  * _text_: Text    The following list contains the coding scheme for the doc_type variable.    * 101: Federal gazette // Draft for public consultation (""Vernehmlassungsverfahren"")  * 102: Federal gazette // Explanation of draft for parliament (""Botschaft"")  * 103: Federal gazette // Strategy, action plan  * 104: Federal gazette // Federal council decree (""Bundesratsbeschluss"")  * 105: Federal gazette // (Simple) Federal decree (""(Einfacher) Bundesbeschluss"")  * 106: Federal gazette // General decree (""Allgemeinverfügung"")  * 107: Federal gazette // Treaty (""Übereinkommen"")  * 108: Federal gazette // Treaty (""Abkommen"")  * 109: Federal gazette // Draft for parliament (""Entwurf"")  * 110: Federal gazette // Report (""Bericht"")  * 111: Federal gazette // Report of parliamentary comission (""Bericht"")  * 112: Federal gazette // Report of federal council (""Bericht"")  * 201: Parl. businesses // Submitted text  * 202: Parl. businesses // Reason text  * 203: Parl. businesses // Federal council response  * 204: Parl. businesses // Initial situation  * 205: Parl. businesses // Proceedings  * 301: Parl. transcripts // Speech of MP  * 302: Parl. transcripts // Speech of federal council  * 401: Federal legislation // Legal text of the official compilation (law, ordinances, etc.)  * 501: Court decisions // Federal Supreme Court  * 502: Court decisions // Federal Criminal Court  * 503: Court decisions // Federal Administrative Court    ## Code folder    The code folder contains all R code for the analyses. The files are numbered chronologically.     * _1_classifier_training.R_: Training of classifiers for classification of policy sectors  * _2_classifier_application.R_: Classification of documents in corpus  * _3_dictionary_application.R_: Biodiversity indexing of documents in corpus  * _4_stm_truncation.R_: Truncation of indexed documents to keep only relevant parts  * _5_stm_translation.R_: Translation of FR and IT documents to DE  * _6_stm_model.R_: Preprocesssing and structural topic model  * _7_plots.R_: Plots and numbers as included in the paper    The code/functions folder contains custom functions used in the scripts, e.g. to support topic model interpretation.    Package versions and setup details are noted in the code files.    ## Contact    Please direct any questions to Ueli Reber (ueli.reber@eawag.ch)."	proprietary
 biofuel_emissions_753_1	SAFARI 2000 Gas Emissions from Biofuel Use and Production, September 2000	ORNL_CLOUD STAC Catalog	2000-09-10	2000-09-16	24.82, -14.86, 24.82, -14.86	https://cmr.earthdata.nasa.gov/search/concepts/C2789024961-ORNL_CLOUD.umm_json	Domestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively. As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.	proprietary
-biogas-aus-hofdunger-in-der-schweiz_1.0	Biogas aus Hofdünger in der Schweiz	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814620-ENVIDAT.umm_json	Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.	proprietary
-biogas-from-animal-manure-in-switzerland_1.0	Biogas from animal manure in Switzerland	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814639-ENVIDAT.umm_json	Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.	proprietary
-biomass_above_ground_of_live_trees-19_1.0	Biomass above ground of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814656-ENVIDAT.umm_json	Dry weight (mass) of the aboveground parts of living trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 biomass_allocation_703_1	Biomass Allocation and Growth Data of Seeded Plants	ORNL_CLOUD STAC Catalog	1922-07-15	2003-07-15	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784383281-ORNL_CLOUD.umm_json	This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric  additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious	proprietary
-biomass_of_live_trees-18_1.0	Biomass of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814713-ENVIDAT.umm_json	Dry weight (mass) of living trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood, branch coarse wood, brushwood/twigs and needles/leaves. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-biomass_of_lying_dead_trees-70_1.0	Biomass of lying dead trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814786-ENVIDAT.umm_json	Dry weight (mass) of dead, lying trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition of the stem, the branch coarse wood.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-biomass_of_lying_dead_wood_lis-72_1.0	Biomass of lying dead wood (LIS)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814909-ENVIDAT.umm_json	Dry weight (mass) of lying deadwood  starting at 7 cm in diameter that does not fulfil the criteria for a tally tree (measurement location of dbh not identifiable or the dbh is less than 12cm).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-biomass_of_standing_dead_trees-69_1.0	Biomass of standing dead trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814961-ENVIDAT.umm_json	Dry weight (mass) of dead, standing trees and shrubs  starting at 12 cm dbh. This consists of the tree parts: roots, stemwood and also, depending on the degree of decomposition, the branch coarse wood.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-biomass_of_total_dead_wood-71_1.0	Biomass of total dead wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814991-ENVIDAT.umm_json	Dry weight (mass) of all deadwood. This consists of the standing dead trees and shrubs  starting at 12cm dbh and the lying deadwood  starting at 7cm in diameter.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 biomdens_450_1	BOREAS TE-18 Biomass Density Image of the SSA	ORNL_CLOUD STAC Catalog	1994-09-02	1994-09-02	-106.52, 53.31, -104.19, 54.44	https://cmr.earthdata.nasa.gov/search/concepts/C2929130809-ORNL_CLOUD.umm_json	This biomass density image covers almost the entire BOREAS SSA.  The pixels for which biomass density is computed include areas that are in conifer land cover classes only.  The biomass density values represent the amount of overstory biomass (i.e., tree biomass only) per unit area.  It is derived from a Landsat-5 TM image collected on 02-Sep-1994.  The technique that was used to create this image is very similar to the technique that was used to create the physical classification of the SSA.	proprietary
 biomebg2_296_1	BOREAS RSS-08 BIOME-BGC SSA Simulations of Annual Water and Carbon Fluxes	ORNL_CLOUD STAC Catalog	1994-01-01	1996-12-31	-111, 49, -89, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2813394229-ORNL_CLOUD.umm_json	Derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration and net primary productivity within the BOREAS SSA-MSA, at a 30 m spatial resolution.  Mode	proprietary
 biomebgc_295_1	BOREAS RSS-08 BIOME-BGC Model Simulations at Tower Flux Sites in 1994	ORNL_CLOUD STAC Catalog	1994-01-01	1994-12-31	-106.2, 53.63, -98.29, 55.9	https://cmr.earthdata.nasa.gov/search/concepts/C2807643677-ORNL_CLOUD.umm_json	BIOME-BGC is a general ecosystem process model designed to simulate biogeochemical and hydrologic processes across multiple scales.  BIOME-BGC is used to estimate daily water and carbon budgets for the BOREAS tower flux sites for 1994.	proprietary
 block_invertebrates_1	A dataset of Antarctic and sub-Antarctic invertebrates	AU_AADC STAC Catalog	1901-12-01	1982-12-29	-155, -84, 180, -38	https://cmr.earthdata.nasa.gov/search/concepts/C1214313344-AU_AADC.umm_json	The dataset was compiled from papers entered into Block's bibliography of invertebrate occurrences in the Antarctic and sub-Antarctic. The dataset provides a comprehensive list of all terrestrial invertebrates recorded from the Antarctic and sub-Antarctic (at that time). Data were entered into an Excel spreadsheet, which contains approximately 3500 entries.  This dataset forms part of the work completed for Australian Antarctic Science (AAS) project 1146 (ASAC_1146) and the RiSCC program, AAS project 1015 (ASAC_1015).  Papers from the Block Bibliography are available as a separate collection in the Australian Antarctic Division Library.  This dataset has also been incorporated into the biodiversity database, which can be found at the provided URL.	proprietary
-bluegreen-ecological-network-data_1.0	Multi-Scale Prioritization framework for Urban Blue-Green Infrastructure Planning to Support Biodiversity: Data & Codes	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.7645874, 47.0925656, 9.0719604, 47.6320819	https://cmr.earthdata.nasa.gov/search/concepts/C3226081654-ENVIDAT.umm_json	This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).	proprietary
-bole_wood_mass_of_live_trees-50_1.0	Bole wood mass of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814548-ENVIDAT.umm_json	Dry weight (mass) of the stemwood with bark of the living trees and shrubs  starting at 12 cm dbh.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-book-of-abstracts-from-plans-to-land-change-dynamics-of-urban-regions_1.0	From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814557-ENVIDAT.umm_json	"Book of abstracts from the  virtual conference ""From Plans to Land Change: Dynamics of Urban Regions""    Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning’s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.    This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.    The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL."	proprietary
 boreas_aeshrday_235_2	BOREAS AES Canadian Hourly and Daily Surface Meteorological Data, R1	ORNL_CLOUD STAC Catalog	1975-01-01	1997-01-01	-107.87, 52.17, -97.83, 57.35	https://cmr.earthdata.nasa.gov/search/concepts/C2759278030-ORNL_CLOUD.umm_json	This data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount.	proprietary
 box_hill_ice_compression_1	Box Hill Ice Compression Tests	AU_AADC STAC Catalog	1977-04-15	1982-03-15	144, -38, 145, -37	https://cmr.earthdata.nasa.gov/search/concepts/C1214308311-AU_AADC.umm_json	"A series of ice compression tests were carried out by Jo Jacka in 1977, and again by J.S.Birch in 1979-82, all aimed at determining how ice reacted under different circumstances. For each series of experiments, five different ""Box Hill"" rigs were set up, and kept at -10C (1977) or -30 (1979) for the duration of the experiments. The experiments in 1977 came to an early end when the cold room being used failed.  The setup and method for each experiment, along with the results, were recorded in log books and have been archived at the Australian Antarctic Division.  Logbook(s): Glaciology Box Hill Compression Rig Experiments, Book 1 - Initial notes on setup, and recordings of early results for the 1977 experiments.  Glaciology Box Hill Compression Rig Experiments, Book 2 - More results from 1977.  Glaciology Ice Compression Logbook - Setup and results for the 1979 experiments."	proprietary
 bratts_penguin_gis_1	Islands NE of Brattstrand Bluff penguin GIS dataset	AU_AADC STAC Catalog	1981-11-01	1982-04-01	77, -69, 77, -69	https://cmr.earthdata.nasa.gov/search/concepts/C1214313310-AU_AADC.umm_json	"Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands.  Update May 2015 -  This dataset has been rename from ""Brattstrand Bluff penguin GIS dataset"" to ""Islands NE of Brattstrand Bluff penguin GIS dataset"" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown."	proprietary
@@ -15722,7 +15467,6 @@ brok_5k_gis_1	Broknes Peninsula 1:5000 Topographic GIS Dataset	AU_AADC STAC Cata
 broknes_lake_catchments_gis_1	Lake catchments on Broknes, Larsemann Hills	AU_AADC STAC Catalog	1997-05-06	2001-08-14	76.285, -69.4193, 76.42, -69.3698	https://cmr.earthdata.nasa.gov/search/concepts/C1214313378-AU_AADC.umm_json	Catchment boundaries of the the lakes on Broknes, Larsemann Hills. These catchments were generated using the FLOWDIRECTION and BASINS routines in the GRID module of ArcInfo GIS.	proprietary
 bromwich_0337948_1	A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5	SCIOPS STAC Catalog	1979-01-01	2002-08-31	-180, -90, 180, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json	This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades.              Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it.              MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html              Please reference the following publication if you use the data in a publication:       Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708.	proprietary
 brownbay_bathy_dem_1	A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands	AU_AADC STAC Catalog	1997-02-01	2000-02-05	110.54, -66.281, 110.548, -66.279	https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json	This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM.   The data are stored in a UTM zone 49 projection.  They were created by interpolation of point data using Kriging.  The input point data comprised soundings and terrestrial contour vertices.  THE DATA ARE NOT FOR NAVIGATION PURPOSES.	proprietary
-bryophyte-observer-bias_1.0	Greater observer expertise leads to higher estimates of bryophyte species richness	ENVIDAT STAC Catalog	2024-01-01	2024-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.umm_json	This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise.    Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon  NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of  bryophyte species richness. _Journal of Vegetation Science_. (submitted)	proprietary
 bunger_east_sat_1	Bunger Hills East Satellite Image Map 1:50 000	AU_AADC STAC Catalog	1992-06-01	1992-06-30	101, -66, 102, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214313379-AU_AADC.umm_json	Satellite image map of Bunger Hills East/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992.  The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
 bunger_geology_gis_1	Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset	AU_AADC STAC Catalog	1980-01-01	1997-12-31	98, -67.5, 102, -65.3	https://cmr.earthdata.nasa.gov/search/concepts/C1214313380-AU_AADC.umm_json	Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset. For additional information, see the published map 'Bunger Hills - Denman Glacier Bedrock Geology', published in 1994, and available at the provided URL.	proprietary
 bunger_hills_contours_1	Bunger Hills contours	AU_AADC STAC Catalog	2007-01-17	2007-01-17	100.249722, -66.426111, 101.585278, -65.9075	https://cmr.earthdata.nasa.gov/search/concepts/C1380160706-AU_AADC.umm_json	Fifty metre interval contours were derived for cartographic purposes from a Digital Elevation Model (DEM) of the Bunger Hills created using SPOT 5 HRS satellite imagery acquired 17 January 2007.  The DEM is described by the metadata record 'Bunger Hills SPOT5 DEM (Digital Elevation Model)' with Entry ID bunger_hills_spot5_dem_gis. The DEM was referenced to Mean Sea Level using Earth Gravitational Model 1996 (EGM96). Estimated accuracies of the DEM (confidence level 90%): planimetric - 15 to 30 metres vertical - 10 metres to 20 metres for slope less than or equal to 20 per cent The DEM should be viewed before using the contours as it has some No Data areas.  The contours were created in ArcGIS 10.3 using the following procedure: 1 The DEM was resampled using the Resample tool to a cell size of 50 metres using the bilinear technique;  2 The DEM resulting from step 1 was used as an input to the Focal Statistics tool which was used to calculate, for each cell, the mean elevation of a three cell by three cell square neighbourhood; 3 The Contour tool was used to create 50 metre interval contours from the DEM resulting from step 2; 4 The contours resulting from step 3 were smoothed using the Smooth Line tool with the Paek algorithm and a smoothing tolerance of 50 metres;  5 The contours resulting from step 4 were converted to single part features using the Multipart to Singlepart tool;  6 A topology was created for the contours resulting from step 5 and used to identify contours touching and editing was carried out to correct these errors.	proprietary
@@ -15788,7 +15532,6 @@ canopychem_422_1	Seedling Canopy Chemistry, 1992-1993 (ACCP)	ORNL_CLOUD STAC Cat
 canopyspec_423_1	Seedling Canopy Reflectance Spectra, 1992-1993 (ACCP)	ORNL_CLOUD STAC Catalog	1992-11-06	1993-03-15	-122.05, 37.4, -122.05, 37.4	https://cmr.earthdata.nasa.gov/search/concepts/C2776849767-ORNL_CLOUD.umm_json	The reflectance spectra of Douglas-fir and bigleaf maple seedling canopies were measured. Canopies varied in fertilizer treatment and leaf area density respectively.	proprietary
 capeden_management_gis_1	Cape Denison Management Zone GIS Dataset	AU_AADC STAC Catalog	2004-01-01	2004-12-31	142.651, -67.014, 142.691, -67.003	https://cmr.earthdata.nasa.gov/search/concepts/C1214313393-AU_AADC.umm_json	This GIS dataset is comprised of the boundary of the Visual Protection Zone at Cape Denison, Antarctica. The data were created for the Management Plan for Historic Site and Monument No 77 and Antarctic Specially Managed Area (ASMA) No 3 produced by the Australian Antarctic Division in 2004. The data are formatted according to the SCAR Feature Catalogue and are available for download (see Related URLS). 	proprietary
 capeden_sat_ikonos_1	A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001	AU_AADC STAC Catalog	2001-01-26	2001-01-31	142.5153, -67.0697, 143.03, -66.9478	https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json	The following was done by a contractor  for the Australian Antarctic Division:  A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created  by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then  (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and   (ii) georeferenced.  See the Quality section for details about the satellite images and the georeferencing.  The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details.  Three satellite image maps were produced from the georeferenced mosaic.  See the SCAR Map Catalogue entries in Related URLs for details.	proprietary
-carabid-beetles-in-forests_2.0	Carabid beetles in forests	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814572-ENVIDAT.umm_json	Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types.    Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG.    Data are provided on request to contact person against bilateral agreement.	proprietary
 casey_alk_clones_1	Alkane mono-oxygenase genes from marine sediment near Casey	AU_AADC STAC Catalog	2001-12-01	2001-12-25	110.3, -66.35, 110.35, -66.3	https://cmr.earthdata.nasa.gov/search/concepts/C1214313396-AU_AADC.umm_json	This dataset consists of 67 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with word-processing as well as sequence analysis software.   The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG.   The library was created from a marine sediment sample that was part of the SRE4 marine biodegradation experiment in O'Brien bay near Casey station. The sample used was from the sediment exposed to Special Antarctic Blend diesel 5-weeks after the time of deployment.   These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments.	proprietary
 casey_aws_1	Automatic Weather Station Data from Casey	AU_AADC STAC Catalog	1996-04-11		110, -66, 110, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214313356-AU_AADC.umm_json	The automatic weather stations at the Australian stations (Casey, Davis, Macquarie Island, and Mawson) were installed by the Bureau of Meteorology. They collect information on the following (in the following units):  date  Time hh:mm  wind speed knots  wind direction degrees  air temperature degrees celsius  relative humidity percent  air pressure hPa  Times are in UT.  Measurements are made at 4 metres.  The fields in this dataset are: date time (hh:mm) wind speed (knots) wind direction (degrees) air temperature (degrees celsius) relative humidity (percent) air pressure (hPa)  More current data are provided at the AWS data page at the provided URL.  A download file is available from the provided URL which provides information about the locations where wind measurements at Casey have been made. The information was provided to David Smith of the Australian Antarctic Data Centre by Phil Smart of the Hobart office of the Bureau of Meteorology in February 2009. David added the coordinates and the information about their origin.	proprietary
 casey_biopiles_DSM_2013_1	Digital Surface Model of the biopiles and nearby area at Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 10 February 2013	AU_AADC STAC Catalog	2013-02-10	2013-02-10	110.5211, -66.2822, 110.5261, -66.2808	https://cmr.earthdata.nasa.gov/search/concepts/C1214308483-AU_AADC.umm_json	The Digital Surface Model (DSM) was created by Dr Arko Lucieer of TerraLuma (http://www.terraluma.net/) and the University of Tasmania for the Terrestrial and Nearshore Ecosystems research group at the Australian Antarctic Division (TNE/AAD). An orthophoto was also created. See the metadata record 'Orthophoto of the biopiles and nearby area at Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 10 February 2013' with ID 'casey_biopiles_ortho_2013'.  The products were requested for Australian Antarctic Science Project 4036:  Remediation of petroleum contaminants in the Antarctic and subantarctic. The products were created from digital photos taken on the 10th February, 2013, with a Canon EOS 550D  from a Mikrokopter Oktokopter piloted by Arko Lucieer and Zybnek Malenovsky.  The products were georeferenced to ground control points surveyed using differential GPS by Dr Daniel Wilkins of TNE/AAD. Raw photo metadata: ISO-400, Focal Length 20mm, f/6.3 Exposure Time 1/1250 sec. Horizontal Datum: ITRF2000.	proprietary
@@ -15800,41 +15543,27 @@ caseybathy_gis_1	Bathymetry of Approaches to Casey Station	AU_AADC STAC Catalog
 casfair1_gis_1	Casey RAN Fair Sheet Data from HI 161 V5/500 6610/1 scale 1:10 000	AU_AADC STAC Catalog	1990-12-15	1991-03-10	110.364, -66.268, 110.497, -66.231	https://cmr.earthdata.nasa.gov/search/concepts/C1214313314-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 161 V5/500 6610/1 scale 1:10 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.	proprietary
 casfair2_gis_1	Casey RAN Fair Sheet Data from HI 161 V5/500 6610/2 scale 1:25 000	AU_AADC STAC Catalog	1990-12-15	1991-03-10	110.463, -66.268, 110.571, -66.244	https://cmr.earthdata.nasa.gov/search/concepts/C1214313405-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 161 V5/500 6610/2 scale 1:25 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.	proprietary
 casfair3_1	Casey RAN Fair Sheet Data from HI 189 V5/584 6610/1 scale 1:25 000	AU_AADC STAC Catalog	1992-12-27	1993-02-28	110.153, -66.334, 110.51, -66.198	https://cmr.earthdata.nasa.gov/search/concepts/C1214313359-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Casey Station. This fair sheet, HI 189 V5/584 6610/1 scale 1:25 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID caseybathy_gis.	proprietary
-catchment-biodiversity-vaud-edna_1.0	Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	6.8579407, 46.1876586, 7.3303528, 46.6289126	https://cmr.earthdata.nasa.gov/search/concepts/C3226081799-ENVIDAT.umm_json	This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.	proprietary
-causal-effect-of-lup_1.0	Causal effect of LUP	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	116.4484859, 23.9449666, 118.6127926, 25.7295192	https://cmr.earthdata.nasa.gov/search/concepts/C2789814593-ENVIDAT.umm_json	Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.    Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.     Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.   XZQDM2 is villages' unique administrative ID;  Area is the land area of village i;  Dis2water is the Euclidean distance from village i to the nearest waterbody;  Dis2coastl is the Euclidean distance from village i to the nearest coastline;  Dis2city is the the Euclidean distance from village i to the city center;  Dis2county is the the Euclidean distance from village i to the nearest county center;  Elevation is the the average elevation within village i;  Dis2road is the the Euclidean distance from village i to the nearest road;  Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;  Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;  Intensity is the percentage of land that was assigned to the development-permitted zones in village i;   Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;  BuLE is the dependent variable, representing built-up land expansion in village i in year t;  Town is town' unique administrative ID.     Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend  assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.	proprietary
-causal-effect-of-mfoz_1.0	Causal effect of MFOZ	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	116.2441398, 23.5080862, 120.7485344, 27.512657	https://cmr.earthdata.nasa.gov/search/concepts/C2789814608-ENVIDAT.umm_json	Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?    Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020).     Data: Data.xlsx contains the variables of 954 towns in Fujian Province.   Town_ID is the town unique ID;   County_ID is the county unique ID;  City_ID is the city unique ID;  MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);  Builtup_13_15 is the built-up land expansion from 2013 to 2015;   Builtup_13_18 is the built-up land expansion from 2013 to 2018;  Builtup_13_20 is the built-up land expansion from 2013 to 2020;  Dis2water is the Euclidean distance from the town to the nearest waterbody;  Slope is the the average slope within the town;  GDP is the average GDP in 2010 within the town;   Pop is the average population in 2010 within the town;  Road is the average population in 2010 within the town;  Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;  Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.    Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.	proprietary
 cb54bd70826842a9acf658ebabe4a104_NA	ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1	FEDEO STAC Catalog	2002-01-01	2011-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143053-FEDEO.umm_json	This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for SCIAMACHY in 2008.	proprietary
 cc4d85ee-6c72-4249-8775-a96e359457ad_1	Global template for the GLASOD digital database	CEOS_EXTRA STAC Catalog	1991-07-01	1991-07-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2232848766-CEOS_EXTRA.umm_json	"The Global Assessment of Human Induced Soil Degradation (GLASOD) was conducted by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, as commissioned by the United Nations Environment Programme (UNEP).  ISRIC produced a 1:10 million scale wall chart in 1990 and subsequently produced a digital data set. In essence, the GLASOD database contains information on soil degradation within map units as reported by numerous soil experts around the world through a questionnaire.  It includes the type, degree, extent, cause and rate of soil degradation.  From these data, the GRID-Nairobi center produced digital and hardcopy maps and made area calculations.   The GLASOD database includes a topographic basemap or global template of continental coastlines, islands and lakes, which GRID-Nairobi extracted from the digital version of GLASOD's 1:10 million wall map.  All of the boundaries that defined oceans and lakes were selected to create a new ARC/INFO coverage, which was subsequently used as a basemap for all the maps in UNEP's World Atlas of Desertification (see reference below).   The global boundaries template contains 306 polygons of four types, which are coded in the data set as follows: 1) Oceans; 2) Lakes; 3) Continents; and 4) Islands.  It is available from GRID as a single ARC/INFO 'EXPORT'-format file comprising 1.7 Mb when uncompressed. While the original projection ISRIC used for the GLASOD wall map was the Mercator to display the various continents with as little distortion as possible, it is distributed by GRID in either the Van der Grinten (a variation of Mercator) or the Geographic projection.   The sources of the global boundaries template are ISRIC and UNEP/GRID, and the proper references are as follows:   Oldeman, L. R., Hakkeling, R. T. A. and W. G. Sombroek.  October 1990. ""World Map of the Status of Human-Induced Soil Degradation; Explanatory Note"".  (The) Global Assessment of Soil Degradation, ISRIC and UNEP in cooperation with the Winand Staring Centre, ISSS, FAO and ITC; 27 pages.   Deichmann, Uwe and Lars Eklundh.  July 1991. ""Global digital data sets for land degradation studies: a GIS approach"".  GRID Case Study Series No. 4; UNEP/GEMS & GRID; Nairobi, Kenya; 103 pages (mostly pp. 29-32). An additional reference is UNEP's 1992 World Atlas of Desertification (Edward Arnold, London, UK, 69 pages - see pages vii to ix). "	proprietary
 ccamlr_subareas_gis_1	CCAMLR Statistical Reporting Subareas GIS Dataset.	AU_AADC STAC Catalog	2002-06-01		-180, -90, 180, -40	https://cmr.earthdata.nasa.gov/search/concepts/C1214313406-AU_AADC.umm_json	CCAMLR (Commission for the Conservation of Antarctic Marine Living Resources) Statistical Reporting Subareas. GIS data representing the boundary (line) and centroid (point with the area name as an attribute) of each area. The southern boundary of the areas adjacent to Antarctica is the coastline of Antarctica. The coastline has not been included with this data.  This dataset is no longer maintained by the Australian Antarctic Data Centre as the CCAMLR Statistical Reporting Subarea boundaries are now available from CCAMLR's Online GIS (see the Related URL).	proprietary
 ccbeb356a88847058159049678fe5c35_NA	ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1	FEDEO STAC Catalog	2004-01-01	2010-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142673-FEDEO.umm_json	This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for ACE in 2008.	proprietary
-ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch_1.0	CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.806475, 46.832964, 9.806475, 46.832964	https://cmr.earthdata.nasa.gov/search/concepts/C2789814623-ENVIDAT.umm_json	__Cloud Condensation Nuclei (CCN) data:__ A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).    __Hygroscopicity data:__ The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).    __Predicted cloud droplet numbers:__ Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).	proprietary
 cdcb0605afa74885a66d8be0fdd2ed24_NA	ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ensemble product), Version 2.6	FEDEO STAC Catalog	2002-07-24	2012-04-08	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143205-FEDEO.umm_json	The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. For further details about these data products please see the documentation.	proprietary
 cden_artefacts_gis_1	Commonwealth Bay Artefacts Survey Data, October - December 2002	AU_AADC STAC Catalog	2002-10-01	2002-12-31	142.6576, -67.0132, 142.6888, -67.0043	https://cmr.earthdata.nasa.gov/search/concepts/C1625714420-AU_AADC.umm_json	These data were collected during an AAD conservation expedition to Mawson's  Huts, Cape Denison, Commonwealth Bay, Antarctica in 2002 (October to  December).  The expedition travelled to Commonwealth Bay on board the  Astrolabe (French Antarctic supply ship).  An expedition report was written, a large number of photographs were taken,  and a large number of artefacts were catalogued.  Several GIS shapefiles were created from these data.  They are point, line and  poygon data showing the location of the artefacts.	proprietary
 cden_gis_1	Cape Denison Historic Site, Commonwealth Bay - GIS DataSet Digitised from Cape Denison Map	AU_AADC STAC Catalog	1985-12-01	1990-01-01	142, -67, 143, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214313315-AU_AADC.umm_json	Cape Denison, Commonwealth Bay, GIS dataset is a topographic database detailing huts, penguins and natural features such as moraine and lakes. The dataset includes a 5m contour interval.  These shapefiles were obtained by digitising an existing Cape Denison historical map.  All information about natural features, biota, etc are sourced from the map. Note, there is more recent data, or better quality data available with other Cape Denison datasets.	proprietary
 cden_survey_gis_1	Cape Denison Detail Survey, December 1985, GIS Dataset	AU_AADC STAC Catalog	1985-12-01	1985-12-31	142.65, -67.02, 142.7, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313407-AU_AADC.umm_json	This GIS dataset was derived from a detail survey of Cape Denison, Antarctica by G. Crispo in December 1985. Features include coastline, contours, buildings and structures, lakes, areas of exposed rock and penguin colonies.  See AAD File 00/802.	proprietary
-ceilometer-klosters_1.0	Ceilometer Klosters	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.880413, 46.869019, 9.880413, 46.869019	https://cmr.earthdata.nasa.gov/search/concepts/C2789814641-ENVIDAT.umm_json	Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.	proprietary
 century_vemap_m4_820_1	CENTURY: Modeling Ecosystem Responses to Climate Change, Version 4 (VEMAP 1995)	ORNL_CLOUD STAC Catalog	1995-01-01	1995-01-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2956545669-ORNL_CLOUD.umm_json	The CENTURY model, Version 4, is a general model of plant-soil nutrient cycling that is being used to simulate carbon and nutrient dynamics for different types of ecosystems including grasslands, agricultural lands, forests and savannas. CENTURY is composed of a soil organic matter/ decomposition submodel, a water budget model, a grassland/crop submodel, a forest production submodel, and management and events scheduling functions.	proprietary
 cfe3102659f34d33b123b2a0043e4068_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the  Jakobshavn Glacier between 2017-06-03 and 2017-09-08, generated using Sentinel-2 data, v1.1	FEDEO STAC Catalog	2017-06-02	2017-09-08	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142588-FEDEO.umm_json	This dataset contains optical ice velocity time series and seasonal product of the Jakobshavn Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-03 and 2017-09-08.  It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing.  The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway.	proprietary
-ch2014_1	Alpine3D simulations of future climate scenarios CH2014	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	8.227, 46.79959, 8.227, 46.79959	https://cmr.earthdata.nasa.gov/search/concepts/C2789814657-ENVIDAT.umm_json	# Overview    The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.    # Experimental Setup    Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).    # Data    Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.	proprietary
-challenging-the-sustainability-of-urban-beekeeping-evidence-from-swiss-cities_1.0	Challenging the sustainability of urban beekeeping: evidence from Swiss cities	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814684-ENVIDAT.umm_json	Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations)  in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2)  (Dataset 2) aggregated data to model the sustainability of urban beekeeping.	proprietary
 charter-mux-1_NA	CHARTER - MUX	INPE STAC Catalog	2024-01-01	2024-05-14	-56.8245255, -32.3378628, -50.412633, -26.9804791	https://cmr.earthdata.nasa.gov/search/concepts/C3108204161-INPE.umm_json	This collection contains images from the CBERS-4/MUX over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.	proprietary
 charter-wfi-1_NA	CHARTER - WFI	INPE STAC Catalog	2024-04-20	2024-05-20	-62.117385, -35.238325, -44.055968, -25.46713	https://cmr.earthdata.nasa.gov/search/concepts/C3108204160-INPE.umm_json	This collection contains images from the WFI sensor onboard the satellites CBERS-4, CBERS-4A and AMAZONIA-1 over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.	proprietary
 charter-wpm-1_NA	CHARTER - WPM	INPE STAC Catalog	2023-12-27	2024-05-14	-52.08743, -31.457375, -50.412963, -26.980703	https://cmr.earthdata.nasa.gov/search/concepts/C3108204419-INPE.umm_json	This collection contains images from the CBERS-4A/WPM over Brazil. The data is processed by the Disasters Charter and provided as Cloud Optimized GeoTIFF (COG). This products has four spectral bands: Blue, Green, Red and NIR.	proprietary
 charybdis_sat_1	Charybdis Glacier Satellite Image Map 1:500 000	AU_AADC STAC Catalog	1991-09-01	1991-09-30	59, -70, 67, -70	https://cmr.earthdata.nasa.gov/search/concepts/C1214313421-AU_AADC.umm_json	Satellite image map of Charybdis Glacier, Mac. Robertson Land, Antarctica. This map is part (c) in a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991.  The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot/tracks, stations/bases, and glaciers/ice shelves. The map has only geographical co-ordinates.	proprietary
-chelsa-climatologies_2.1	Climatologies at high resolution for the earth’s land surface areas	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789814735-ENVIDAT.umm_json	High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.   &nbsp;    CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.     __Paper Citation:__   > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017)  [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._	proprietary
-chelsa_cmip5_ts_1.0	High resolution monthly precipitation and temperature timeseries for the period 2006-2100	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	180, -90, -180, 84	https://cmr.earthdata.nasa.gov/search/concepts/C2789814811-ENVIDAT.umm_json	Predicting future climatic conditions at high spatial resolution is essential for many applications in science. Here we present data for monthly time series of precipitation and minimum and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation sums at ~5km spatial resolution globally for the years 1850-2100. We validated the performance of the downscaling algorithm by comparing model output with observed climates for the years 1950-2069.     CHELSA_cmip5_ts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.	proprietary
-chelsa_trace_1.0	CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	179.995693, -89.9959722, -179.9959722, 83.9956937	https://cmr.earthdata.nasa.gov/search/concepts/C2789814958-ENVIDAT.umm_json	High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.     The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.	proprietary
-chelsacruts_1.0	CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789814896-ENVIDAT.umm_json	CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum.  Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.    CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.	proprietary
 chem_26_1	Canopy Chemistry (OTTER)	ORNL_CLOUD STAC Catalog	1989-08-23	1991-06-04	-123.94, 44.29, -121.33, 45.06	https://cmr.earthdata.nasa.gov/search/concepts/C2804747736-ORNL_CLOUD.umm_json	Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions	proprietary
 chesapeake_val_2013_0	2013 Chesapeake Bay measurements	OB_DAAC STAC Catalog	2013-04-11		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.umm_json	2013 Chesapeake Bay measurements.	proprietary
 chlorophyll_65-02_1	Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002	AU_AADC STAC Catalog	1965-11-23	2002-12-08	100.147, -54.985, 137.95, 24.567	https://cmr.earthdata.nasa.gov/search/concepts/C1214313422-AU_AADC.umm_json	The variation in the phytoplankton biomass over a decadal time scale, and its relationship with the Antarctic Circumpolar Wave (ACW) and climate change, has been poorly interpreted because of the limited satellite chlorophylla (chl a) data compared with the physical parameters from satellite.  We analysed a long-term chl a dataset along the Japanese Antarctic Research Expedition (JARE) cruise tracks since 1965 to investigate inter-annual variation of phytoplankton biomass.  In the Southern Ocean, increasing trends of chl a and the spreading of higher chl a area to the north with 3-7 year cycles were found.  Although relationships between the decadal change in chl a and climate change such as variation of sea ice extent and the El Nino are still obscure, large variation of primary production in proportion to the chl a is implied.  The chl a concentration of sea surface water has been measured routinely on board the icebreakers Fuji and Shirase during almost every cruise of the JARE.  The download file contains chlorophyll a data collected from ship tracks on JARE voyages between 1965 and 2002.  The field in this dataset are:  Date (local time) Year Latitude Longitude Corrected Chlorophyll a  See the attached paper for more details.  The publications on the data collected during the 1965-1976 and 1988-1993 cruises are listed in Fukuchi [1980] and Suzuki and Fukuchi [1997], respectively.  For data on the 1977-1985 and 1994-1997 cruises, see [Kanda and Fukuchi, 1979; Fukuchi and Tamura, 1982; Tanimura, 1981; Watanabe and Nakajima, 1983; Ino and Fukuchi, 1984; Sasaki, 1984; Hamada et al., 1985; Fukuda et al., 1986; Hattori and Fukuchi, 1988; Midorikawa et al., 2000]. Data post 1998-2002 cruises is in Hirawake and Fukuchi [2004].  Data from the 1986-1987 will be published in the JARE data report of digital media, including all cruise data.  Auxiliary Material for paper 2004GL021394 Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002. Toru Hirawake, Tsuneo Odate and Mitsuo Fukuchi (National Institute of Polar Research, Tokyo) Geophys. Res. Lett., Vol (Num), doi:10.1029/2004GL021394   All of the chl a data have been reported in the publications of the National Institute of Polar Research (NIPR).	proprietary
-chm-hp-4rtm_1.0	Forest canopy structure data for radiation and snow modelling (CH/FIN)	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.871859, 46.845432, 26.6365886, 67.366827	https://cmr.earthdata.nasa.gov/search/concepts/C2789814990-ENVIDAT.umm_json	This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include:     * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each)  * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2)      These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication:    _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_    This publication must be cited when using the data.     ### See also:   For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy)    The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572)	proprietary
-climate-change-scenarios-at-hourly-resolution_1.0	Dataset for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814547-ENVIDAT.umm_json	In fall 2019, a new set of climate change scenarios has been released for   Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are  provided at daily resolution. We produced from the CH2018 dataset a new   set of climate change scenarios temporally downscaled at hourly resolution.   In addition, we extended this dataset integrating the meteorological stations   from the Inter-Cantonal Measurement and Information System (IMIS) network,   an alpine network of automatic meteorological stations operated by the WSL   Institute for Snow and Avalanche Research SLF.    The extension to the IMIS network is obtained using a Quantile Mapping approach   in order to perform a spatial transfer of the CH2018 scenarios from the location   of the  MeteoSwiss stations to the location of the IMIS stations. The temporal   downscaling is performed using an enhanced Delta-Change approach. This approach   is based on objective criteria for assessing the quality of the determined delta   and downscaled time series. In addition, this method also fixes a flaw of common   quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling)   related to the decrease of correlation between different variables. The idea behind   the delta change approach is to take the main seasonal signal (and mean) from   climate change scenarios at daily resolution and to map it to a historical time series   at hourly resolution in order to modify the historical time series. The obtained time   series exhibit the same seasonal signal as the original climate change time series,   while it keeps the sub-daily cycle from the historical time series.    The applied methods (Quantile Mapping and Delta-Change) have limitations in   correctly representing statistically extreme events and changes in the frequency   of discontinuous events such as precipitation. In addition, the sub-daily cycle in   the data is inherited from the historical time series, so there is no information   of the climate change signal in this sub-daily cycle. A careful reading of the paper   accompanying the dataset is necessary to understand the limitations and scope of   application of this new dataset.    This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).	proprietary
 climate_iceberg_1	Antarctic CRC and Australian Antarctic Division Climate Data Set - Australian iceberg observations	AU_AADC STAC Catalog	1978-12-13	2001-03-20	-160, -70, 45, -40	https://cmr.earthdata.nasa.gov/search/concepts/C1214313409-AU_AADC.umm_json	This dataset contains iceberg observations collected routinely on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis.  The observations were made each austral summer from the 1978/1979 season until the 2000/2001 season. Data included voyage number, date, time, latitude, longitude, sea ice concentration, water temperature, total icebergs, number of icebergs in each width category, the width to height ratio of selected larger tabular icebergs. It was been compiled and presented on the web by the Glaciology program of the Antarctic CRC (now ACE CRC).	proprietary
 climate_pressure_1	ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure	AU_AADC STAC Catalog	1901-01-01	1998-12-31	-180, -80, 180, -17	https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json	This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002.	proprietary
 climate_sea_ice_1	Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice	AU_AADC STAC Catalog	1973-01-18	1996-12-19	-180, -80, 180, -50	https://cmr.earthdata.nasa.gov/search/concepts/C1214313423-AU_AADC.umm_json	This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice.  Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996.  Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available.	proprietary
 climate_temps_1	ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures	AU_AADC STAC Catalog	1901-01-01	2002-12-31	-180, -80, 180, -17	https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json	This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002.	proprietary
-climatological-snow-data-1998-2022-oshd_1.0	Climatological snow data since 1998, OSHD	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.umm_json	This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).	proprietary
 climwat_Not provided	CLIMWAT, A Climatic Database	CEOS_EXTRA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2232283619-CEOS_EXTRA.umm_json	CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the ready calculation of crop water requirements, irrigation supply and irrigation scheduling for various crops for a range of climatological stations worldwide.              The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries. CLIMWAT is published as Irrigation and Drainage paper No 49 in 1994 and includes a Manual with description of the use of the database with CROPWAT The data are contained in five diskettes included in the publication and can be ordered as FAO Irrigation and Drainage Paper 49 through the FAO Sales and Marketing Group.              [Summary provided by the FAO.]	proprietary
 cmar_wh_Not provided	CSIRO Marine Data Warehouse (OBIS Australia)	CEOS_EXTRA STAC Catalog	1978-02-05	1997-08-30	114, -44, 155, -8	https://cmr.earthdata.nasa.gov/search/concepts/C2226653616-CEOS_EXTRA.umm_json	The CSIRO Marine Data Warehouse is a repository for biological and other marine survey data collected by CSIRO Division of Marine and Atmospheric Research (CMAR), Australia. It contains field (observational) data from numerous research trawls and other fisheries-related surveys conducted in waters around Australia by the Division since the late 1970s. At time of writing (April 2006) the database is serving approximately 106,000 species-level records to OBIS. Multiple species records and those of taxa not identified to species level are presently excluded. Associated data include species counts and/or weights in some but not all cases.	proprietary
 cmimpacts_1	UND Cloud Microphysics IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-25	2022-02-25	-90.429, 33.261, -64.987, 47.275	https://cmr.earthdata.nasa.gov/search/concepts/C1997744632-GHRC_DAAC.umm_json	The UND Cloud Microphysics IMPACTS dataset consists of cloud particle measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The UND Cloud Microphysics IMPACTS dataset files are stored in ASCII format from January 25, 2020 through February 26, 2020, and from January 6, 2022 through February 25, 2022.	proprietary
@@ -15846,22 +15575,12 @@ cmx3srl_1	CAMEX-3 SCANNING RAMAN LIDAR V1	GHRC_DAAC STAC Catalog	1998-08-06	1998
 co2_emissions_1deg_1021_1	ISLSCP II Carbon Dioxide Emissions from Fossil Fuels, Cement, and Gas Flaring	ORNL_CLOUD STAC Catalog	1950-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785301251-ORNL_CLOUD.umm_json	This data set contains decadal (1950, 1960, 1970, 1980, 1990 and 1995) estimates of gridded fossil-fuel emissions, expressed in 1,000 metric tons C per year per one degree latitude by one degree longitude. The CO2 emissions are the summed emissions from fossil-fuel burning, hydraulic cement production and gas flaring. The years 1950 to 1990 were developed and compiled using somewhat different procedures and information than the 1995 data. The national annual estimates (Boden et al., 1996) from 1950 to 1990 were allocated to one degree grid cells based on gridded information on national boundaries and political units, and a 1984 gridded human population map (Andres et al., 1996). For the 1995 data, the population data base developed by Li (1996a) and documented by CDIAC (DB1016: Li, 1996b) was used as proxy to grid the 1995 emission estimates. There is one *.zip data file with this data set at 1.0 degree spatial resolution.	proprietary
 combined_ancillary_xdeg_1200_1	ISLSCP II Land and Water Masks with Ancillary Data	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785331161-ORNL_CLOUD.umm_json	This data set contains the ISLSCP II fixed land/water masks and percentages of land or water in each cell. There are seven zip data files: four produced from a 1-km land/water mask compiled at the Jet Propulsion Laboratory (JPL) in support of NASA's Earth Observing System;  two files of a land outline overlay created from the land/water mask files created at NASA's Goddard Space Flight Center; and one file which is a latitude grid coordinate file and longitude grid coordinate file produced by the ISLSCP II staff. All of these data are provided at three spatial resolutions of .25, 0.5 and 1-degree in latitude and longitude and on a common Earth grid.	proprietary
 comm_alfred_1	GPS data points taken at Commonwealth Bay by Alfred Wilklemayer	AU_AADC STAC Catalog	1997-01-01	1997-12-31	142.1, -67.1, 142.72, -67.05	https://cmr.earthdata.nasa.gov/search/concepts/C1214313425-AU_AADC.umm_json	"Six GPS data points collected by Alfred Wilklemayer, taken during a one year expedition at Commonwealth Bay, Antarctica.  GPS Points collected at Commonwealth Bay, Antarctica, during 1997  The following GPS data points were collected opportunistically by Mr Alfred Wilklemayer, during a one year expedition in Commonwealth Bay, Antarctica.   Identification Object	Position Frozen Husky Dog 67 degrees 04'07"" S, 142 degrees 42'39"" E First Canister 67 degrees 03'69"" S, 142 degrees 42'10"" E Second Canister 67 degrees 03'74"" S, 142 degrees 42'10"" E Third Can/Stick 67 degrees 03'28"" S, 142 degrees 42'09"" E Furthest Point In (during expedition) 67 degrees 05'47"" S, 142 degrees 40'02"" E Furthest Point West (during expedition) 67 degrees 04'06"" S, 142 degrees 06'04"" E"	proprietary
-community-structure-life-history-traits-and-performance-traits-of-urban-cnbw_1.0	Community structure, life-history traits and performance traits of urban cavity-nesting bees annd wasps	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	0.2197266, 46.890732, 28.3886719, 59.0864909	https://cmr.earthdata.nasa.gov/search/concepts/C3226081815-ENVIDAT.umm_json	# Background  Urban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we provide the data used to investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. To do so, we installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts).  The dataset contains: * The taxonomic metrics on CNBW * The taxonomic metrics on the natural enemies from CNBW * The life-history traits and performance traits	proprietary
 comp_runoff_monthly_xdeg_994_1	ISLSCP II UNH/GRDC Composite Monthly Runoff	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784897571-ORNL_CLOUD.umm_json	The University of New Hampshire (UNH)/Global Runoff Data Centre (GRDC) composite runoff data combines simulated water balance model runoff estimates derived from climate forcing with monitored river discharge. It can be viewed as a data assimilation applied in a water balance model context (conceptually similar to the commonly used 4DDA techniques used in meteorological modeling). Such a data assimilation scheme preserves the spatial specificity of the water balance calculations while constrained by the more accurate discharge measurement. There are 11 data files in this data set and 1 changemap file which shows the differences between the ISLSCP II land/water mask and the original data set.	proprietary
-content-coding-of-exemption-approval-decisions-for-forest-clearances_1.0	Content coding of exemption approval decisions for forest clearances	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814566-ENVIDAT.umm_json	"The Federal Office for the Environment (FOEN) is responsible for granting exemptions for forest clearances that in principle are prohibited in Switzerland. Initiators of infrastructure projects have to submit an examption approval request to the cantonal forest administration which has to inform the FOEN. The FOEN thus administers a dataset of forest clearance requests and approval decisions that can be requested there. This dataset contains information on a coding of the content of all the forest clearance requests between 2001 and 2017, that elicits whether the reason for the clearance can be attributed to ""sustainable economy"" objectives such as ""green economy"", ""bioeconomy"" and ""circular economy""."	proprietary
-convection-in-snow_1.0	snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	6.5678716, 46.5207841, 6.5678716, 46.5207841	https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json	snowpackBuoyantPimpleFoam is a  two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).	proprietary
-convection-in-snow_1.0	snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	6.5678716, 46.5207841, 6.5678716, 46.5207841	https://cmr.earthdata.nasa.gov/search/concepts/C2789814580-ENVIDAT.umm_json	snowpackBuoyantPimpleFoam is a  two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).	proprietary
-core_0.1	Cloud Optimized Raster Encoding (CORE) format	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4546554, 47.3605425, 8.4546554, 47.3605425	https://cmr.earthdata.nasa.gov/search/concepts/C2789814594-ENVIDAT.umm_json	"__DISCLAIMER__: CORE is still in development. Interested parties are warmly invited to join common development, to comment, discuss, find bugs, etc.    __Acknowledgement:__ The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing ​HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).    __License:__ The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 ""No Rights Reserved"" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.    -----------------------    __Summary:__  The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).    __WARNING__: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').    With this early public sharing of the format we explicitly support the Open Science agenda, which implies __""shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process""__ (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).    __CORE Specifications:__     1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)    2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)    Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).    3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;     Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.    4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)     5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer    6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)    7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)    __Major issues to be solved:__    - Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.    - Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.    - Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)    __Example__    __Notice__: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].     For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).    For full reproducibility, we provide the original data set and results, as well scripts for data encoding and extraction (see resources)."	proprietary
-correct-observer-bias-only-sdms_1.0	Novel methods to correct for observer and sampling bias in presence-only species distribution models	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	4.9658203, 42.7416347, 17.5341797, 48.2197941	https://cmr.earthdata.nasa.gov/search/concepts/C2789814610-ENVIDAT.umm_json	Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.     Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.    Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.	proprietary
 cosmirimpacts_1	Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-15	2022-02-28	-116.701, 30.5854, -62.6816, 48.5552	https://cmr.earthdata.nasa.gov/search/concepts/C1995565150-GHRC_DAAC.umm_json	The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) IMPACTS dataset consists of brightness temperature measurements collected by the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) flown onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. CoSMIR is a conical and cross-track scanning radiometer with frequencies centered at 50.3, 52.8, 89.0, 165.5, 183.31±1, 183.31±3, and 183.31±7 GHz. The brightness temperature data from CoSMIR are available from January 15, 2020 through February 28, 2022 in netCDF-4 format.	proprietary
-cosmo-wrf-documentation_1.0	Running COSMO-WRF on very-high resolution over complex terrain	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	7.31281, 45.4, 10.6311, 48.2535	https://cmr.earthdata.nasa.gov/search/concepts/C2789814624-ENVIDAT.umm_json	This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.	proprietary
 cossirimpacts_1	Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR) IMPACTS	GHRC_DAAC STAC Catalog	2023-01-05	2023-03-02	-115.701, 26.395, -66.647, 49.36	https://cmr.earthdata.nasa.gov/search/concepts/C3104921929-GHRC_DAAC.umm_json	The Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR) IMPACTS dataset consists of data measured onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The CoSSIR dataset consists of measured ice clouds and brightness temperatures, water vapor profiles, and snowfall rates. CoSSIR data are available from January 5, 2023, through March 2, 2023, in netCDF-4 format.	proprietary
 cp_lidar_images_721_1	SAFARI 2000 Cloud Physics Lidar (CPL) Quicklook Images and Maps	ORNL_CLOUD STAC Catalog	2000-08-17	2000-09-25	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788400595-ORNL_CLOUD.umm_json	The effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote-sensing and in-situ measurements of clouds and aerosols. is Data products include: (1) cloud profiling with 30-m vertical and 200-m horizontal resolution at 1064 nm, 532 nm, and 355 nm;(2) aerosol, boundary layer, and smoke plume profiling;(3) optical depth estimates (column and by layer); and(4) extinction profiles. The CPL provides information to permit a comprehensive analysis of radiative and optical properties of optically thin clouds. Data users are asked to read and abide by the CPL data usage policy found at [http://virl.gsfc.nasa.gov/cpl/cpl_register.htm].	proprietary
 cplimpacts_1	Cloud Physics LiDAR (CPL) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-15	2023-03-02	-117.23, 26.907, -64.894, 48.657	https://cmr.earthdata.nasa.gov/search/concepts/C1995565938-GHRC_DAAC.umm_json	The Cloud Physics LiDAR (CPL) IMPACTS dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR (CPL) onboard the NASA ER-2 high-altitude research aircraft in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in HDF-5 format from January 15, 2020, through March 2, 2023.	proprietary
-crack-propagation-in-weak-snowpack-layers-insights-from-high-speed-photography_1.0	"Dataset for ""Dynamic crack propagation in weak snowpack layers: Insights from high-resolution, high-speed photography"""	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.8698783, 46.8076829, 9.8698783, 46.8076829	https://cmr.earthdata.nasa.gov/search/concepts/C2789814649-ENVIDAT.umm_json	This data set includes material and results described in the related research article:    Bergfeld, B., van Herwijnen, A., Reuter, B., Bobillier, G., Dual, J., and Schweizer, J.: Dynamic crack propagation in weak snowpack layers: Insights from high-resolution, high-speed photography, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-360, in review, 2021.    # Context:   In order to study crack propagation in weak snowpack layers in great detail, we recorded Propagation Saw Test (PST) experiments using a high-speed camera and applied digital image correlation (DIC) to derive displacement and strain fields in the slab, weak layer, and substrate. We demonstrated the versatility and accuracy of the DIC method by showing measurements from three PST experiments, resulting in slab fracture, crack arrest and full propagation in the related publication.   # Content:  - Supplementary material for related publication  - Ilustrative videos showing crack propagation  - High-speed recordings of the Experiments   (the raw .cine files are available upon request)  Processed Data containing:     - displacement, velocity and acceleration fields for the three PSTs     - speed and touchdown dataset	proprietary
-crack-propagation-speeds-in-weak-snowpack-layers_1.0	Crack propagation speeds in weak snowpack layers from three events: PST, whumpf and slab avalanche	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.8700437, 46.807722, 9.8700437, 46.807722	https://cmr.earthdata.nasa.gov/search/concepts/C2789814659-ENVIDAT.umm_json	For the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into the underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie, for scales between 5 and 25 meters, we used accelerometers placed on the snow surface, and for scales below 5 meters, we performed a Propagation Saw Test. The mean crack speeds ranged from 36 ± 6 to 49 ± 5 m s^{-1}, and did not exhibit scale dependence. Using the Discrete Element Method and the Material Point Method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales.  This dataset includes raw data as well as crack speed estimates from the three crack propagation events.   Where possible, we reproduced these field experiments with numerical models based on Discrete Element Method (DEM, Bobillier and others, 2020 and 2021) and Material Point Method (MPM. Gaume and others, 2018 and Trottet and others, 2021). The input parameters of the models were estimated from the corresponding snow profiles conducted at each test site.    ## The raw data include:  * Propagation Saw Test movie with mechanical fields derived from Digital image Correlation analysis of the recording  * Acceleration data recorded with wireless time synchronized accelerometers placed on the snow surface during crack propagation in a whumpf.  *Video of an artificially triggered avalanche with widespread crack propagation. The video was used to georeference surface cracks in order to estimate crack propagation time and distance, providing crack propagation speed estimates.  * Snow profile recorded at each test site    ## Experimental crack speed estimates include:  * Crack speed evolution within the first meters derived from the Propagation Saw Test.  * Crack speeds estimated from the time delay of the collapse, observed between different accelerometers during crack propagation of a whumpf.  * Crack speed estimates from video analysis of the artificially triggered avalanche.    ## Reproduced crack speeds using the DEM an MPM model:  * Modelled Propagation Saw Test using MPM (2D and 3D system) and DEM.  * Modelled whumpf using MPM (beam and areal configuration)  * Modelled avalanche using MPM (beam and areal configuration)    Beside the movies (mp4 format), all data is either provided as netCDF files or excel sheets (see readme file), depending on the amount of data. A detailed description of the three crack propagation events and how crack speed was derived, can be found in the related publication:        ### References for applied models:   Bobillier, G., B. Bergfeld, A. Capelli, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2020. Micromechanical modeling of snow failure. The Cryosphere, 14(1): 39-49.    Bobillier, G., B. Bergfeld, J. Dual, J. Gaume, A. van Herwijnen and J. Schweizer 2021. Micro-mechanical insights into the dynamics of crack propagation in snow fracture experiments. Scientific Reports, 11: 11711.    Gaume, J., T. Gast, J. Teran, A. van Herwijnen and C. Jiang 2018. Dynamic anticrack propagation in snow. Nature Communications, 9(1): 3047.    Trottet, B., R. Simenhois, G. Bobillier, A. van Herwijnen, C. Jiang and J. Gaume 2021. From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release. EGU General Assembly 2021, Online, 19-30 Apr 2021, EGU21-8253.	proprietary
 cramer_leemans_637_1	SAFARI 2000 Mean Climatology, 0.5-Deg, 1930-1960, V[ersion]. 2.1 (Cramer and Leemans)	ORNL_CLOUD STAC Catalog	1931-01-01	1960-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2804823994-ORNL_CLOUD.umm_json	This data set is a subset of Cramer and Leeman's (1999) global mean monthly climatology . The subset is for the area of southern Africa within the following bounds: 5 N to 35 S and 5 E to 60 E. The data are available in ASCII grid and binary image formats.	proprietary
-cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data_1.5	Cropland and grassland map of Switzerland based on Sentinel-2 data	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814690-ENVIDAT.umm_json	We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland,  sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.	proprietary
 cropland_612_2	NPP Cropland: Gridded Estimates For the Central USA, 1982-1996, R1	ORNL_CLOUD STAC Catalog	1982-01-01	1996-12-31	-99.75, 38.25, -83.25, 48.75	https://cmr.earthdata.nasa.gov/search/concepts/C2751948433-ORNL_CLOUD.umm_json	This data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell. The magnitude and interannual variation in NPP was estimated using crop area and yield data from the U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The major harvested commodities were corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay. Total NPP estimates include both above- and below-ground components. County-level NPP in 1992 ranged from 195 to 760 gC/m2/year. The area of highest NPP, ranging from 650 to 760 gC/m2/year, was found in a band extending across Iowa, through northern Illinois, Indiana, and southwestern Ohio. Areas of moderate NPP, from 550 to 650 gC/m2/year, occurred mostly in Michigan and Wisconsin, while large areas of low NPP, from 200 to 550 gC/m2/year, occurred in North Dakota, southern Illinois, and Minnesota. The area of highest production was also the area with the largest proportion of land sown with corn and soybean. NPP for counties in Iowa varied among years (1982-1996) by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods). Revision Notes: The documentation for this data set has been modified, and the data files have been reformatted. The data files have been checked for accuracy and the contents are identical to those originally published in 2001. 	proprietary
 crsimpacts_1	Cloud Radar System (CRS) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-25	2023-02-28	-95.46, 31.073, -64.894, 48.658	https://cmr.earthdata.nasa.gov/search/concepts/C1995871269-GHRC_DAAC.umm_json	The Cloud Radar System (CRS) IMPACTS dataset consists of calibrated radar reflectivity, Doppler velocity, linear depolarization ratio, and normalized radar cross-section estimates collected by the Cloud Radar System (CRS) onboard the NASA ER-2 high-altitude research aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The CRS IMPACTS dataset files are available from January 25, 2020, through February 28, 2023, in HDF-5 format.	proprietary
 cru_monthly_climate_xdeg_1014_1	ISLSCP II CRU05 Climate Time Series for Global Land Areas, 1986-1995	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785289548-ORNL_CLOUD.umm_json	This data set contains monthly climate time series data created by the Climatic Research Unit (CRU) at the University of East Anglia, U.K.,for every year covering the period 1986 to 1995. This time series is a subset of a larger CRU monthly data set that covers the period of 1901 to 1996.  The data comprise a suite of six climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapor pressure, and cloud cover. There are 13 files in this data set provided at 0.5 and 1.0 degree spatial resolutions. 	proprietary
@@ -15875,35 +15594,10 @@ d545d232-ac86-49c3-a42d-67b0b9608b29_NA	METOP GOME-2 - Cloud Top Pressure (CTP)
 d6d0d7b4cf3540448b4ddcaed2f54b81_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 4.2	FEDEO STAC Catalog	1997-09-03	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143153-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 4.2 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites).  Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).	proprietary
 d9df331e346f4a50b18bcf41a64b98c7_NA	ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0	FEDEO STAC Catalog	1982-01-01	2019-06-30	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142699-FEDEO.umm_json	This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.The SCFV time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years.	proprietary
 da2b8512312a4f14a928766f7f632d36_NA	ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ORAC algorithm), Version 4.01	FEDEO STAC Catalog	2002-05-20	2012-04-08	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142735-FEDEO.umm_json	The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on ENVISAT, derived using the ORAC algorithm, version 4.01. Both daily and monthly gridded products are availableFor further details about these data products please see the linked documentation.	proprietary
-daily-500m-gridded-net-radiation-and-soil-moisture-for-switzerland-2004_1.0	daily 500m gridded net radiation and soil moisture for Switzerland, 2004	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814737-ENVIDAT.umm_json	R data set containing R raster objects with 500m gridded daily modeled soil moisture and net radiation covering Switzerland for the year 2004.	proprietary
-daily-solute-and-isotope-of-stream-water-and-precipitation_1.0	Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.7092058, 47.04259, 8.75655, 47.1507977	https://cmr.earthdata.nasa.gov/search/concepts/C2789814801-ENVIDAT.umm_json	This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.  The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.  An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242	proprietary
 daily_precip_est_793_1	SAFARI 2000 Daily Rainfall Estimates, 0.1-Deg, Southern Africa, 1993-2001	ORNL_CLOUD STAC Catalog	1993-01-01	2001-12-31	10, -34, 50, 0	https://cmr.earthdata.nasa.gov/search/concepts/C2789731186-ORNL_CLOUD.umm_json	The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 401 lines, each with 341 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv.	proprietary
-dalmolin_thurmodeling1_1.0	Data for: Understanding dominant controls on streamflow spatial variability to set-up a semi-distributed hydrological model: the case study of the Thur catchment	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.5830688, 47.1112614, 9.6377563, 47.6246779	https://cmr.earthdata.nasa.gov/search/concepts/C2789814894-ENVIDAT.umm_json	This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre–alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two–stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe.	proprietary
-danger_descriptions_avalanche_bulletin_switzerland_1.0	How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland?	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.8886719, 45.7984239, 10.5908203, 47.6804285	https://cmr.earthdata.nasa.gov/search/concepts/C2789814949-ENVIDAT.umm_json	The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020.	proprietary
 darling_sst_00_Not provided	2000 Seawater Temperatures at the Darling Marine Center	SCIOPS STAC Catalog	2000-01-01	2000-12-31	-71.31, 42.85, -66.74, 47.67	https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json	2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine	proprietary
 darling_sst_01_Not provided	2001 Seawater Temperatures at the Darling Marine Center	SCIOPS STAC Catalog	2001-01-01	2001-04-20	-71.31, 42.85, -66.74, 47.67	https://cmr.earthdata.nasa.gov/search/concepts/C1214612276-SCIOPS.umm_json	2001 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center Walpole, Maine.	proprietary
 darling_sst_82-93_Not provided	1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center	SCIOPS STAC Catalog	1982-03-01	1993-12-31	-71.31, 42.85, -66.74, 47.67	https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json	Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine	proprietary
-data-amphibian-monitoring_1.0	Data from: Estimation of breeding probbability can make monitoring data more revealing: a case study of amphibians	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814986-ENVIDAT.umm_json	"This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the ""first"" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 (""second"" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021)."	proprietary
-data-analysis-toolkits_1.0	Data analysis toolkits	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814544-ENVIDAT.umm_json	"These are condensed notes covering selected key points in data analysis and statistics.  They were developed by James Kirchner for the course ""Analysis of Environmental Data"" at Berkeley in the 1990's and 2000's.  They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!    License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license."	proprietary
-data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0	Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.9761734, 46.5670779, 8.9761734, 46.5670779	https://cmr.earthdata.nasa.gov/search/concepts/C3226081971-ENVIDAT.umm_json	"## Summary    * Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland    * R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis    ## Data  The dataset included here is the ""natural"" reservoir inflow for the Luzzone reservoir.  Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).    ## Codes  R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:    * GEV (Generalized Extreme Value)    * GPD and GPDd (Generalized Pareto Distribution including declustered version)    * eGPD (extended Generalized Pareto Distribution)    * MEV (Metastatistical Extreme Value)"	proprietary
-data-broedlin-cnp_1.0	Data Broedlin CNP	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.2944336, 49.2539427, 12.3706055, 52.8695717	https://cmr.earthdata.nasa.gov/search/concepts/C2789814562-ENVIDAT.umm_json	Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.	proprietary
-data-code-link-and-metadata-on-forward-scattering-of-snow-at-totalp_1.0	Data, Code Link and Metadata on Forward Scattering of Snow at Totalp	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.8051, 46.82699, 9.83738, 46.83847	https://cmr.earthdata.nasa.gov/search/concepts/C2789814579-ENVIDAT.umm_json	"### Overview  We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.  Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.    ### Format  The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.     ### Structure  In Data_Forward_Scattering.zip you will find all necessary data and model details to   reproduce the results of the JGR publication ""How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels""     - 	__Model Input Data__ contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.   -  __Model Code__ contains the full model Alpine3d including the radiative transfer module GROUNDEYE.   -  __Model Output Data__ contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.   -  __Measurements Solar Testsite__ contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.   -  __Postprocessing__ contains all R-Scripts used for the analysis and   plotting of the corresponding data.     In each of these folders you will find detailed information in   the file 'About this Folder.txt'."	proprietary
-data-for-huelsmann_et_al_ecol_appl_2016_1.0	Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	5.8447266, 45.7521934, 24.0023804, 53.917281	https://cmr.earthdata.nasa.gov/search/concepts/C2789814613-ENVIDAT.umm_json	The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.    ## Inventory data  The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.    ## Mortality predictors  A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.     To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.    ## Further information  For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.	proprietary
-data-for-numerical-investigation-of-sediment-yield_1.0	Data for Numerical Investigation of Sediment Yield Underestimation in Supply-Limited Mountain Basins with Short Records	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.3127747, 46.0721826, 7.8923035, 46.4841158	https://cmr.earthdata.nasa.gov/search/concepts/C2789814626-ENVIDAT.umm_json	The dataset contains the input and output files from the publication by Hirschberg et al. (2022).    The input files are the climate forcing time series generated with the AWE-GEN model. The output files include the hydrological outputs, which is the same for scenarios 1-6 considered in Hirschberg et al. (2022), and the sediment-related outputs, whereas the transport-limited scenario 6 is included in the output of scenario 1.    The input file includes:    - time _D_ (h)  - precipitation _Pr_ (mm/h)  - atmospheric temperature _Ta_ (°C)  - incoming shortwave radiation _Rsw_ (W/m^2)  - cloudiness _N_ (-)    The output files include:    - hydrological outputs (accroding to time in input and normalized by basin area)      - total discharge _Q_ (mm/h)      - surface discharge _Qs_ (mm/h)      - subsurface discharge _Qss_ (mm/h)      - soil water storage _Vw_ (mm)      - snow depth _snow_ (mm SWE)      - snow depth change _snowacc_ (mm/h SWE)      - potential evapotranspiration _PET_ (mm/h)      - actual evapotranspiration _AET_ (mm/h)    - sediment outputs (accroding to time in input and normalized by basin area)      - hillslope landslide magnitude _ls_ (mm/h)      - channel sediment storage _sc_ (mm)      - hillslope sediment storage _sh_ (mm)      - total sediment discharge _so_ (mm/h)      - transport-limited total sediment discharge _sopot_ (mm/h)      - sediment discharge by debris flows _dfs_ (mm/h)      - transport-limited sediment discharge by debris flows _dfspot_	proprietary
-data-from-hagen-skeels-etal-pnas_1.0	Data from: Earth history events shaped the evolution of uneven biodiversity across tropical moist forests	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814651-ENVIDAT.umm_json	Datasets and R scripts     ~~~~~Datasets    Dataset_S1.csv: Distribution of species diversity in plant and vertebrate clades. Total clade level diversity and species diversity in tropical moist forests (TMF) across the Neotropics, Indomalaya and Afrotropics. Pantropical clades are found in all three TMF regions with at least one-third of the clades’ total diversity spread throughout these regions. Pantropical diversity disparity (PDD) clades show lower diversity in TMF in the Afrotropics than in the Neotropics and Indomalaya.    Dataset_S2.csv: Environmental and species richness data across 110 km x 110 km grid cells in Neotropical, Indomalayan and Afrotropical moist forest sites. Variables include x and y coordinates in the Behrmann equal area coordinate reference system, potential evapotranspiration (PET), mean annual temperature (MAT), mean annual precipitation (MAP), amphibian, mammal, bird and squamate reptile species richness and biogeographic region, as well as the first two principal components of a principal component analysis on PET, MAT and MAP (PC1, PC2).    Dataset_S3.csv: Global reconstructed paleo-temperature estimates and spatial coordinates across 200 million years at 170,000 year intervals at 2 degree spatial resolution.    Dataset_S4.csv: Gen3sis model parameters and biodiversity summary statistics. Summary statistics include the number of extant species, the number of extinct species, the total number of species, the number of species within the tropical moist forest biome boundaries in the Neotropics, the Afrotropics and Indomalaya, the pantropical index, and the pantropical disparity index, as well as the running time-step and diversity of unfinished simulations.    Dataset_S5.csv: Net relatedness index (NRI) values for vertebrate clades showing an observed disparity in pantropical diversity in the Neotropical, Indomalayan and Afrotropical moist forest regions and associated P-values. Positive values indicate phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion.    ~~~~~Scripts    Script_1 - GLS.R. R script to replicate the linear modelling analyses.    Script_2 - Gen3sis_config_template.R. R script to generate the configurations files to run the simulation experiment.    Script_3 - Gen3sis_config_creator.R. R script to generate the configurations files to run the simulation experiment.	proprietary
-data-hagenmoos-1989-2020_1.0	Restoration of the lowland raised bog Hagenmoos (Switzerland): Data on vegetation, ecological indicator values and species richness from 1989 until 2020	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.5183311, 47.2336995, 8.5245109, 47.2365551	https://cmr.earthdata.nasa.gov/search/concepts/C2789814669-ENVIDAT.umm_json	This dataset includes data from three vegetation surveys in a restored raised bog (Hagenmoos) in the lowland of the canton of Zürich (Switzerland). The bog Hagenmoos was restored by cutting shrubs and trees within the formerly peat-cutting pits and by blocking drainages. The vegetation surveys were carried out before (1989), ten years after (1999) and 30 years after restoration (2020). In each vegetation survey, all vascular plant and bryophyte species within 72 permanent plots were recorded. Of these plots, 34 are located within the formerly peat-cutting pits and 38 are located outside the peat pits. Based on presence-absence data of vascular plants and bryophytes, mean ecological indicator and strategy values based on Landolt et al. (2010) were calculated and are provided in the Excel sheet. Indicator values for light, moisture, pH, nutrients, humus, temperature and continentality and strategy values for stress, competition and ruderality were considered. Furthermore, species richness for the following groups were calculated: (1) all plant species, (2) all vascular plant species, (3) bog specialists among vascular plant species, (4) all bryophyte species, (5) bog specialists among bryophyte species. As bog specialist species, we considered all plant species listed as characteristic species of raised bogs by Feldmeyer-Christe and Küchler (2018: Moore der Schweiz. Haupt, Bern).	proprietary
-data-of-national-dishes-their-similarity-and-trade-flows_1.0	Data of national dishes, their similarity and trade flows	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814795-ENVIDAT.umm_json	The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.	proprietary
-data-on-wild-bee-taxonomic-and-functional-diversity-in-switzerland_1.0	Data on wild bee taxonomic and functional diversity in Switzerland	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081828-ENVIDAT.umm_json	"Raw data supporting the paper ""Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients"". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:  - Alpha taxonomic community metrics: species richness and Shannon diversity  - Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.  - Community weighted means of 8 functional traits   - The local community contributions on the functional and taxonomic beta diversity (LCBD).    The dataset also includes the following:  - The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).  -The three types of protected areas, defined according to the protective measures.  - The model evaluation, variable importance and partial dependece data."	proprietary
-data-set-of-mee-20-04-264_1.0	Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814549-ENVIDAT.umm_json	The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.	proprietary
-data-snow-instability_1.0	Data set on snow instability	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.7318268, 46.735343, 9.9652863, 46.8707071	https://cmr.earthdata.nasa.gov/search/concepts/C2789814573-ENVIDAT.umm_json	These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.    Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:  Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.	proprietary
-data_ecolappl_2020_1.0	Grassland restoration: insects and insect traits	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.5198975, 47.4359836, 8.6489868, 47.4944726	https://cmr.earthdata.nasa.gov/search/concepts/C2789814592-ENVIDAT.umm_json	This dataset contains all data, on which the following publication below is based.    **Paper Citation**:    Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)    Please cite this paper together with the citation for the datafile.    # Methods    ## Study site  The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).  In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:    - **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).  - **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).  - **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).    Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.    ## Arthropod sampling  Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),    ## Functional traits  We used two sets of functional traits in this study.      **Morphometric traits**:    Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.      **Life-history traits**    Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.        ### References    Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.      Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.      MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.      Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.      Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.      Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.	proprietary
-data_jae_2019_1.0	Grassland restoration: nematodes and plant communities	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.4766388, 47.397776, 8.751297, 47.5008591	https://cmr.earthdata.nasa.gov/search/concepts/C2789814728-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    __Paper Citation:__   > Resch, M.C., Schütz, M., Graf, U., Wagenaar, R., van der Putten, W.H., Risch, A.C. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56: 1782-1793.    Please cite this paper together with the citation for the datafile.    # Methods  ## Study area and experimental settings  The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.  The three restoration treatments used were:   i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.  ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.   iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.   “Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.   Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years).     ## Nematode and plant sampling  Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.   We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).   The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).    ## References  Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.  Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.  Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627  Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9  Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.  Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.  Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4  Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.  Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.  MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.  Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.  Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.   Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004	proprietary
-data_wet_aval_model_1.0	Weather, snowpack and avalanche occurrence data for automated prediction of wet-snow avalanche activity	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081504-ENVIDAT.umm_json	Datasets used to implement the wet-snow avalanche activity model presented in the article:    Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., van Herwijnen, A., Schweizer, J. (2023). Automated prediction of wet-snow avalanche activity in the Swiss Alps. Journal of Glaciology, under review    Each dataset includes the input variables (weather and snowpack features) and the target variable (wet-snow  avalanche day or not) used to build the model. Additionally, Dataset3_nowcast and Dataset3_forecast contain the predictions provided by the RF12 model.    All input variables are described in the Appendix of the article and also in the read_me file. Further information on SNOWPACK variables is also available at https://models.slf.ch/p/snowpack/ .	proprietary
-database-on-holdover-time-of-lightning-ignited-wildfires_1.0.0	Database on holdover time of lightning-ignited wildfires	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.4545978, 47.3606372, 8.4545978, 47.3606372	https://cmr.earthdata.nasa.gov/search/concepts/C3226081111-ENVIDAT.umm_json	This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.	proprietary
-dataset-for-future-water-temperature_1.0	Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814970-ENVIDAT.umm_json	This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.  This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).	proprietary
-dataset-for-ogrs-2018-publication_1.0	Dataset for OGRS 2018 publication	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815000-ENVIDAT.umm_json	"This dataset contains the road and plot data used for the geospatial analysis example showcased in ""Fostering Open Science at WSL with the EnviDat Environmental Data Portal"", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.   The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually."	proprietary
-dataset-of-the-socio-cultural-forest-monitoring-switzerland-wamos2-wamos3_1.0	Dataset of the Socio-cultural Forest Monitoring Switzerland for WaMos2 and WaMos3	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081334-ENVIDAT.umm_json	"This repository consists of the merged data from WaMos2 (2010) and WaMos2 (2020) and also includes both Corona-related surveys that have been conducted within the phase of WaMos3.     WaMos3 is the third assessment of the relationship of the Swiss population to the forest after 1997 and 2010 and was conducted in 2020. As in WaMos2 in 2010, the attitude of the population to the forest as a recreation area, to wood production and to the protective and ecological functions were examined. The topic of climate change was also included. In addition, the views of adolescents between 15 and 18 years of age were taken into account for the first time.     A detailed description of the provided data  can be found in accompanied file ""WaMos_Metadatenbeschreibung_221027.pdf"" which also contains explanations and examples of the merging process from WaMos2 to WaMos3 as well as sampling procedures. Further, the samples itself can be processed with the help of the provided R-file ""EnviDat_WaMos_dataset.R""."	proprietary
-dataset-on-wind-fields-and-energy-potential-in-swiss-alps_1.0	Dataset on Cosmo-1 based Energy Potential in Swiss Alps	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814559-ENVIDAT.umm_json	This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.    100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.   Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).     Please cite the following paper in connection with the dataset:    __Paper Citation:__   > _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_	proprietary
 davfair1_gis_1	Davis RAN Fair Sheet Data from HI 171 V5/519-6877/9 scale 1:5000	AU_AADC STAC Catalog	1992-02-09	1992-02-12	77.841, -68.561, 77.892, -68.546	https://cmr.earthdata.nasa.gov/search/concepts/C1214313411-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 171 V5/519-6877/91 scale 1:5000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.	proprietary
 davfair2_gis_1	Davis RAN Fair Sheet Data from HI 120 V5/463-6877/1 scale 1:10 000	AU_AADC STAC Catalog	1989-02-06	1989-03-04	77.826, -68.591, 77.967, -68.551	https://cmr.earthdata.nasa.gov/search/concepts/C1214308511-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 120 V5/463-6877/1 scale 1:10 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.	proprietary
 davfair3_gis_1	Davis RAN Fair Sheet Data from HI 120 SUP 1 V5/480-6877/7 scale 1:10 000	AU_AADC STAC Catalog	1990-02-01	1990-02-28	77.796, -68.599, 77.951, -68.558	https://cmr.earthdata.nasa.gov/search/concepts/C1214308512-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Davis Station. This fair sheet, HI 120 SUP 1 V5/480-6877/7 scale 1:10 000, was hand digitised to capture soundings as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID davisbathy_gis.	proprietary
@@ -15927,23 +15621,9 @@ dc8psr_1	CAMEX-3 POLARIMETRIC SCANNING RADIOMETER (PSR) V1	GHRC_DAAC STAC Catalo
 dd3da2570363429791b51120bdd29c02_NA	ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2	FEDEO STAC Catalog	1991-08-05	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142649-FEDEO.umm_json	The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v05.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070	proprietary
 de75072edfca44bfaaec0ed171d86bde_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0	FEDEO STAC Catalog	1997-09-04	2020-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142555-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020.  Note, the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.  (A separate dataset is also available for data on a sinusoidal projection.)	proprietary
 de883c15-85f6-435a-b5aa-3f6468ba919a_1	Annual methane emission from livestock (KG./SQ.KM)	CEOS_EXTRA STAC Catalog	1988-06-01	1988-06-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2232847409-CEOS_EXTRA.umm_json	"This one-degree latitude/longitude spatial resolution data set of Methane Emission from Animals data set was compiled at the NASA/Goddard Institute of Space Studies (GISS) from nine animal population densities.*  The statistics on animal populations came from the Food and Agricultural Organization (FAO) and other sources.  The animals were distributed across a one-degree latitude/longitude grid of national political boundaries, and sub-national boundaries for Australia, Brazil, Canada, China, India, USA and the former USSR.  Published estimates of methane production from each type of animal were applied to the populations to yield a global distribution of annual methane emission by animals, expressed in kilograms per square kilometer of CH4 produced annually.   A large spatial variability in the distribution of methane production (and the source animal populations) can clearly be seen in the global digital map.  The total annual global estimate of methane emission is 75.8 teragrams (10 to the 12th power), about 55% of which is found between 25 degrees North and 55 degrees North latitude, a significant contribution to the observed north-south gradient of atmospheric methane concentration.  The proper reference to this data set is ""J. Lerner, E. Matthews and I. Fung, June 1988.  Methane Emission from Animals: a Global High-Resolution Data Base, Global Biogeochemical Cycles, vol. 2, no. 2, pp. 139-156.""   The original magnetic tape containing these data came from the National Center for Atmospheric Research (NCAR-Scientific Computing Division/Data Support Section); 1850 Table Mesa Drive; Boulder, Colorado; 80307 USA. This tape contains the methane emission data file and ten animal population density data files (the nine listed below plus bovines, a combination of 'cattle' and 'dairy cows').  In addition it has three listing or program files; all of the data and non-data files are in ASCII format.  While all of the 14 files have been read from tape to disk at GRID-Geneva, only the annual methane emission (kg./sq. km.) data file has been converted to a binary image format.   This data set is available as five different file types:    - ASCII file of complex real (floating-point, 32-bit) numbers,      both original file; and the IBM-compatible file;  - 16-bit, signed integer file;  - eight-bit unsigned integer file;  - demonstration file (also eight-bit), useful only for visualization.   Type number (3) is recommended for most analytical purposes, as it contains all of the numerical information of the original file (1), but is easier to work on.  Type number (4) is only recommended for those systems which cannot handle 16-bit data, and type (5) in cases where an annotated image or photoproduct only is desired.   The Methane data file is held in the Plate Carree (Simple Cylindrical) projection, has a spatial resolution of one degree latitude/longitude and consists of 180 rows (lines) by 360 columns (elements/pixels/ samples) of data.  Its origin point is at 90 degrees North latitude and 180 degrees West longitude, and it extends to 90 degrees South latitude and 180 degrees East longitude.  The two-byte or 16-bit per element data file comprises 130 Kb, and the one-byte file 65 kb.    - Cattle, Dairy cows, Water buffalo, Sheep, Goats, Camels, Pigs, Horses and Caribou "	proprietary
-deadwood-generator_1.0	Deadwood Generator	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.9440727, 47.0239773, 9.011364, 47.0448028	https://cmr.earthdata.nasa.gov/search/concepts/C3226081551-ENVIDAT.umm_json	The here presented code generates discrete three-dimensional, RAMMS::ROCKFALL readable deadwood log files (.pts-format) of windtrown forests, including the pilling effect due to slightly different throw directions.	proprietary
-debris-flow-prediction-based-on-rainfall_1.0	Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	7.5740433, 46.2496507, 7.6626205, 46.3345789	https://cmr.earthdata.nasa.gov/search/concepts/C2789814606-ENVIDAT.umm_json	This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).    ## How to install and run the example    Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:    `$ conda env create -f environment.yml`    `$ conda activate thresholds` or `$ source activate thresholds`    To run an example:    `$ python run_example()`    It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.    ## License  GNU General Public License v3.0	proprietary
-debris-flow-volumes-at-the-illgraben-2000-2017_1.0	Debris-flow volumes at the Illgraben 2000-2017	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	7.5884628, 46.2512413, 7.6440811, 46.3205203	https://cmr.earthdata.nasa.gov/search/concepts/C2789814621-ENVIDAT.umm_json	Debris-flow bulk volumes from the WSL monitoring station.  More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).	proprietary
 deglacial_water_isotope_composite_gicc05_1	Antarctic Ice Core Deglacial Water Isotope Composite Record on GICC05	AU_AADC STAC Catalog	1994-07-01		112.8, -81.67, 0.07, -66.77	https://cmr.earthdata.nasa.gov/search/concepts/C1214313450-AU_AADC.umm_json	Precise information on the relative timing of north-south climate variations is a key to resolving questions concerning the mechanisms that force and couple climate changes between the hemispheres. We present a new composite record made from five well-resolved Antarctic ice core records that robustly represents the timing of regional Antarctic climate change during the last deglaciation. Using fast variations in global methane gas concentrations as time markers, the Antarctic composite is directly compared to Greenland ice core records, allowing a detailed mapping of the inter-hemispheric sequence of climate changes. Consistent with prior studies the synchronized records show that warming (and cooling) trends in Antarctica closely match cold (and warm) periods in Greenland on millennial timescales. For the first time, we also identify a sub-millennial component to the inter-hemispheric coupling. Within the Antarctic Cold Reversal the strongest Antarctic cooling occurs during the pronounced northern warmth of the Bolling. Warming then resumes in Antarctica, potentially as early as the Intra-Allerod Cold Period, but with dating uncertainty that could place it as late as the onset of the Younger Dryas stadial. There is little-to-no time lag between climate transitions in Greenland and opposing changes in Antarctica. Our results lend support to fast acting inter-hemispheric coupling mechanisms including recently proposed bipolar atmospheric teleconnections and/or rapid bipolar ocean teleconnections.  The five cores used in the Antarctic deglacial water isotope composite are: Law Dome, Byrd, EPICA Dronning Maud Land (EDML), Siple Dome and Talos Dome. The data for each core is interpolated to 20 year time steps and standardised with respect to its own mean and standard deviation over the interval 9000 to 21000 years before 1950 AD (9-21 ka BP 1950). Estimated dating uncertainty in the composite (relative to GICC05) is +/- 220 y during the interval 10-13 ka BP, +/- 200 y during the interval 13-15 ka BP and +/- 380 y during the interval 15-18 ka BP. Refer to Pedro et al., (in press) (Table 2) and original references (below) for dating uncertainties in the individual cores.  The locations and original references for the isotope data and transfers to the GICC05 timescale of the 5 Antarctic cores are as follows:  Law Dome Location: 66 degrees 46'S 112 degrees 48'E Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for d18O data: 1. Pedro et al., (in press) 2. Morgan, V., Delmotte, M., van Ommen, T. D., Jouzel, J., Chappellaz, J., Woon, S., Masson-Delmotte,, V., and Raynaud, D.: Relative timing of deglacial climate events in Antarctica and Greenland, Science, 297, 1862-1864, 2002.  Byrd Location: 80degrees 01'S 119 degrees 31'W Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for d18O data:  Blunier, T., and Brook, E. J.: Timing of millennial-scale climate change in Antarctica and Greenland during the last glacial period, Science 291, 109-112, 2001.  Siple Dome** Location: 81 degrees 40'S 148 degrees 49'W Reference for transfer to GICC05 timescale: Pedro et al., (in press) Reference for dD data**:  Brook, E. J., White, J. W. C., Schilla, A. S. M., Bender, M. L., Barnett, B. Severinghaus, J. P., Taylor, K. C., Alley, R. B., and Steig, E. J.: Timing of millennial-scale climate change at Siple Dome, West Antarctica, during the last glacial period, Quat. Sci. Rev., 24, 1333-1343, 2005. **Note: As d18O values for Siple Dome during the deglaciation were not available we used appropriately scaled dD i.e. (dD-10)/8 (on advice of James White and Edward Brook, pers. comm. March 2011).  EDML Location: 75 degrees 00'S 00 degrees 04'E Reference for transfer to GICC05 timescale: Lemieux-Dudon, B., Blayo, E., Petit, J. -R., Waelbroeck, C., Svensson, A, Ritz, C., Barnola, J. -M., Narcisi, B. M., and Parrenin F.: Consistent dating for Antarctic and Greenland ice cores, Quat. Sci. Rev., 29, 8-20, 2010. Reference for d18O data: EPICA community members: One-to-one coupling of glacial climate variability in Greenland and Antarctica, Nature, 444, 195-198, 2006.  Talos Dome Location: 72 degrees 49'S 159 degrees 11'E Reference for transfer to GICC05 timescale: Buiron, D., Chappellaz, J., Stenni, B., Frezzotti, M., Baumgartner, M.,Capron, E., Landais, A., Lemieux-Dudon, B., Masson-Delmotte, V., Montagnat, M., Parrenin, F., and Schilt, A.: TALDICE-1 age scale of the Talos Dome deep ice core, East Antarctica, Clim. Past, 7, 1--16, doi:10.5194/cp-7-1-2011,2011.  Reference for d18O data: Stenni, B., Buiron, D., Frezzotti, M., Albani, S., Barbante, C., Bard, E., Barnola, J. M., Baroni, M., Baumgartner, M., Bonazza, M., Capron, E., Castellano, E., Chappellaz, J., Delmonte, B., Falourd, S., Genoni, L., Iacumin, P., Jouzel, J., Kipfstuhl, S., Landais, A., Lemieux-Dudon, B., Maggi, V., Masson-Delmotte, V., Mazzola, C., Minster, B., Montagnat, M., Mulvaney, R., Narcisi, B., Oerter, H., Parrenin, F., Petit, J. R., Ritz, C., Scarchilli, C., Schilt, A., Schupbach, S., Schwander, J., Selmo, E., Severi, M., Stocker, T. F., and Udisti, R.: Expression of the bipolar see-saw in Antarctic climate records during the last deglaciation, Nature Geoscience, 4, 46-49, doi:10.1038/ngeo1026, 2011.  This work was done as part of AAS 757.	proprietary
 diatoms_sre3_1	Diatom and associated data for examining the effects of heavy metal and petroleum hydrocarbon contamination on benthic diatom communities in Antarctica	AU_AADC STAC Catalog	1997-09-01	1999-03-31	110.45, -66.5, 110.7, -66.2	https://cmr.earthdata.nasa.gov/search/concepts/C1214308436-AU_AADC.umm_json	Full title:  Diatom and associated data for a manipulative field experiment examining the effects of heavy metal and petroleum hydrocarbon contamination on benthic diatom communities in the Windmill Islands, Antarctica.  A manipulative field experiment was performed to assess the effects of heavy metals and petroleum hydrocarbons on benthic diatom communities in the Windmill Islands.  Three treatments were used (control, metal contaminated, and petroleum hydrocarbon contaminated), with replicates of each treatment deployed at three locations (Sparkes Bay, Brown Bay and O'Brien Bay).  The datasets associated with this experiment include the concentrations of metals and hydrocarbons within samples, as well as diatom data (raw counts, and the relative abundance of benthic species).  This work was completed as part of ASAC project 1130 (ASAC_1130) and project 2201 (ASAC_2201).  Public summary from project 1130:  Algal mats grow on sea floor in most shallow marine environments.  They are thought to contribute more than half of the total primary production in many of these areas, making them a critical food source for invertebrates and some fish.  We will establish how important they are in Antarctic marine environments and determine the effects of local sewerage and tip site pollution.  We will also investigate the impact on the algal mats of the additional UV radiation which results from the ozone hole.  Public summary from project 2201:  As a signatory to the Protocol on Environmental Protection to the Antarctic Treaty Australia is committed to comprehensive protection of the Antarctic environment. This protocol requires that activities in the Antarctic shall be planned and conducted on the basis of information sufficient to make prior assessments of, and informed judgements about, their possible impacts on the Antarctic environment. Most of our activities in the Antarctic occur along the narrow fringe of ice-free rock adjacent to the sea and many of our activities have the potential to cause environmental harm to marine life. The Antarctic seas support the most complex and biologically diverse plant and animal communities of the region. However, very little is known about them and there is certainly not sufficient known to make informed judgements about possible environmental impacts.  The animals and plants of the sea-bed are widely accepted as being the most appropriate part of the marine ecosystem for indicating disturbance caused by local sources. Attached sea-bed organisms have a fixed spatial relationship with a given place so they must either endure conditions or die. Once lost from a site recolonisation takes some time, as a consequence the structure of sea-bed communities reflect not only present conditions but they can also integrate conditions in the past. In contrast, fish and planktonic organisms can move freely so their site of capture does not indicate a long residence time at that location. Because sea-bed communities are particularly diverse they contain species with widely differing life strategies, as a result different species can have very different levels of tolerance to stress; this leads to a range of subtle changes in community structure as a response to gradually increasing disturbance, rather than an all or nothing response.  This project will examine sea-bed communities near our stations to determine how seriously they are affected by human activities. This information will be used to set priorities for improving operational procedures to reduce the risk of further environmental damage.  The fields in this dataset are:  Species Site Abundance Treatment Type Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Mercury Nickel Silver Tin Zinc Special Antarctic Blend Lube	proprietary
 diffuse_irradiance_791_1	SAFARI 2000 AERONET-derived Diffuse Spectral Irradiance for Eight Core Sites	ORNL_CLOUD STAC Catalog	2000-01-01	2000-12-31	15.91, -25.02, 31.5, -15.44	https://cmr.earthdata.nasa.gov/search/concepts/C2789729353-ORNL_CLOUD.umm_json	This data set contains monthly mean values of diffuse irradiance fraction [f(Ediff), or ratio of diffuse-to-total irradiance] at ground level for a 30-degree solar zenith angle and in seven spectral bands (MODIS bands 1-7) as well broadband visible (400-700 nm), near-infrared (700-3000 nm) and shortwave (400-3000 nm). Values are provided for eight SAFARI 2000 core sites, including Ghanzi/Okwa River Crossing, Maun (Main and Floodplain Towers), Pandamatenga, and Tshane, Botswana; Skukuza, South Africa; Etosha National Park, Namibia; and Mongu, Zambia. The fractions were estimated with the 6S radiative transfer model, given the mean aerosol optical depth (AOT) values from AERONET sunphotometer measurements. Where sunphotometers were not deployed at a SAFARI 2000 core site, the nearest neighbor sunphotometer data were used. A rough estimate of the likely spatial extrapolation error is provided. These data can be used to estimate typical surface albedo (blue sky conditions) from the theoretical black-sky and white-sky albedo values provided in the MODIS albedo product (MOD43), as well as in other applications.Data for all eight sites are contained in one ASCII file, in csv format. The data file provides the ratio of diffuse (atmospherically-scattered) irradiance to total irradiance, both at ground level, for the eight sites in southern Africa. Mean values are provided for each of 12 months in 10 spectral bands between 0.4 and 4.0 microns, computed for a 30-degree solar zenith angle.  The native resolution of the AERONET sunphotometer data varies, but is typically less than 1 hour. Information about the site location, IGBP classification, and nearest AERONET sunphotometer site is also provided.	proprietary
-digitizing-historical-plague_1.0	Digitizing historical plague	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-16.171875, 28.3043807, 46.40625, 67.4749224	https://cmr.earthdata.nasa.gov/search/concepts/C2789814640-ENVIDAT.umm_json	We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.	proprietary
-dischmex-high-resolution-wrf-simulations-and-measurements_1.0	DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.4070435, 46.3969575, 10.5661011, 47.164322	https://cmr.earthdata.nasa.gov/search/concepts/C2789814655-ENVIDAT.umm_json	"The data presented here corresponds to the publication ""Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain"" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication ""The importance of near-surface winter precipitation processes in complex alpine terrain"" (Gerber et al., 2018b). The dataset contains:    * WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).  * StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.  * ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (Bühler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.    For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).    __Publications:__    Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229–243, doi:10.5194/tc-9-229-2015, 2015.    Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, doi:10.5194/tc-12-3137-2018, 2018.    Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.    Gerber, F., and Sharma, V.:  Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018."	proprietary
-dischmex-meteorological-measurements_1.0	DISCHMEX - Meteorological measurements	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.92665, 46.71291, 9.92665, 46.71291	https://cmr.earthdata.nasa.gov/search/concepts/C2789814692-ENVIDAT.umm_json	Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.    Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).  In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.	proprietary
-disdrometer-data-davos-wolfgang_1.0	Disdrometer Data Davos Wolfgang	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789814759-ENVIDAT.umm_json	The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.	proprietary
-disdrometer-data-gotschna_1.0	Disdrometer Data Gotschnagrat	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.849, 46.859, 9.849, 46.859	https://cmr.earthdata.nasa.gov/search/concepts/C2789814886-ENVIDAT.umm_json	A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.	proprietary
-disdrometer_laret_1.0	Disdrometer Data Laret	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.871859, 46.845432, 9.871859, 46.845432	https://cmr.earthdata.nasa.gov/search/concepts/C2789814960-ENVIDAT.umm_json	A laser optical disdrometer (Parsivel&#178; , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.   The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.	proprietary
-dispersal-prevalence-fish-traits-assemblages_1.0	Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	179.0593033, -27.6032369, -160.5813217, 29.000934	https://cmr.earthdata.nasa.gov/search/concepts/C2789814989-ENVIDAT.umm_json	"This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. ""A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages"" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.     The interaction of habitat dynamics with species dispersal abilities could generate gradients  in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations."	proprietary
-distributed-subcanopy-datasets_1.0	Distributed sub-canopy datasets from mobile multi-sensor platforms (CH / FIN, 2018-2019) for hyper-resolution forest snow model evaluation	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	20.0170898, 66.8507192, 22.1264648, 68.2270448	https://cmr.earthdata.nasa.gov/search/concepts/C2789815033-ENVIDAT.umm_json	This dataset contains datasets of sub-canopy meteorological variables acquired in coniferous forest stands in Switzerland (Davos, Engadine) and Finland (Sodankylä) during the winters 2018 and 2019. The data are presented and used in the publication:   Mazzotti, G., Essery, R., Webster, C., Malle, J.,  & Jonas T. (2020)  Process-level evaluation of a high-resolution forest snow model using observations from mobile multi-sensor platforms   Water Resources Research, under review    The above publication must be cited when using this dataset, and the user is referred to the publication for additional detail.     Data are grouped into 4 folders:   1) Point data includes wind speed data measured with stationary meteorological stations  2) Transect data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired with an automated calblecar system along within-stand transects  3) Grid data includes data of incoming short- and longwave radiation, air and snow surface temperature acquired on 40x40m gridded plots using a handheld instrument, as well as snow depth data measured at the same grids.     Canopy structure information derived from hemispherical images is included for each all surveyed locations as well, and an overview of the field sites is provided.	proprietary
-distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0	Distribution maps of permanent grassland habitats for Switzerland	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081223-ENVIDAT.umm_json	We modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances according to the Swiss habitat typology (TypoCH; Delarze et al. 2015) at 10x10 m resolution across Switzerland. The 20 grassland habitat types belong to the following habitat groups: fens, wet meadows, raised bogs, re-seeded and heavy fertilized grasslands, dry grasslands, nutrient-poor alpine and subalpine grasslands, nutrient-rich pastures and meadows as well as fallow grasslands.     We followed a two-step approach:     (1) Ensemble models provide **distribution maps of the 20 individual grassland habitat types**, using training data from various sources. Predictors were Copernicus Sentinel satellite imagery and variables describing climate, soil and topography. The performance of these maps was assessed with the True Skill Statistics and split‐sampling of the data.     Available maps for each grassland habitat: (1) *Map of the median of predicted probability of occurrence*; (2) *Map of the standard deviation of the predicted probability of occurrence* (available upon request); (3) *Binary presence/absence map* (available upon request). For an overview, see *Overview: Maps of the individual grassland habitats*.    (2) **Combined maps**: The individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys.    Available combined maps: Map of the most likely habitat type (M1F; after regional corrections); Map of the second most likely habitat type (M2); Map of the most likely habitat type without regional corrections (available upon request); Map of the weighted median of the predicted probability of occurrence for the most/second most likely habitat type, respectively (available upon request); map of the ratio of the probabilities of occurrence of the most and second most likely habitat types (available upon request)	proprietary
-diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0	Diversity of ground beetles and spiders as well as cynipid oak gall formation on irrigated and non-irrigated plots in a dry mixed Scots pine forest	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.6136971, 46.3021928, 7.6136971, 46.3021928	https://cmr.earthdata.nasa.gov/search/concepts/C2789814550-ENVIDAT.umm_json	In the dry Pfynwald forest a long-term experiment of WSL was initiated in 2003 with a set of irrigated and non-irrigated plots. Forest Entomologie WSL made several investigations, one of them on the effect of irrigation (or conversely of drought) on the biodiversity of epigaeic arthropods such as ground beetles and spiders. In addition, its effects were also assessed by counting galls formed by gall wasps on pubescent oak.	proprietary
-diversity_of_woody_species-36_1.0	Diversity of woody species	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814561-ENVIDAT.umm_json	Index based on the number of tree and shrub species  starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species  starting at 12 cm dbh in the upper layer.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 dlhimpacts_1	Diode Laser Hygrometer (DLH) IMPACTS	GHRC_DAAC STAC Catalog	2023-01-13	2023-02-28	-95.243, 35.753, -67.878, 48.237	https://cmr.earthdata.nasa.gov/search/concepts/C3247876662-GHRC_DAAC.umm_json	The Diode Laser Hygrometer (DLH) dataset is comprised of water vapor mixing ratio measurements as well as relative humidities (both concerning liquid water and ice) which are derived from the water vapor mixing ratio and ambient static temperature and pressure provided by the TAMMS instrument suite. These measurements were made using two separate DLH instruments installed on the NASA P-3B research aircraft, and the data from these instruments were combined to provide the best combination of accuracy, dynamic range, and data coverage. The two DLH instruments are (1) the zenith-mounted system which utilizes an optical path between the zenith port and the aircraft’s vertical tail, and (2) the short-path system, which utilizes an optical path between two fuselage-mounted fins. This dataset was measured during the 2023 campaign of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Earth Venture Suborbital 3 project. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The project aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The DLH data files are available for flights from January 13, 2023, through February 28, 2023, and are in the ASCII format.	proprietary
 doi:10.25921/sta3-3b95_Not Applicable	2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection	NOAA_NCEI STAC Catalog	2014-09-08	2015-05-08	-84.4, 27.7, -83.4, 29.7	https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json	The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies.  All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico.	proprietary
 doi:10.25921/v3a2-m248_Not Applicable	Archival and Discovery of November 27, 1945 Tsunami Event on Marigrams	NOAA_NCEI STAC Catalog	1945-11-15	1945-12-01	66.97, 24.804, 66.97, 24.804	https://cmr.earthdata.nasa.gov/search/concepts/C2105865668-NOAA_NCEI.umm_json	These water level data were digitized from a scanned marigram image associated with the tsunami event of 1945-11-27 at a tide gauge located at Karachi, Pakistan, and referenced to station datum.  The Karachi marigram is one of the two instrumental records existing of the 1945 Makran tsunami and spans most of the 16 days between November 15 and December 1.  The original Karachi analog record belongs to the Survey of India (SOI) and was collected and digitized by the National Institute of Oceanography (NIO) and Indian National Center for Ocean Information Services (INCOIS) for use in the publication of a few scientific papers.  This digital marigram scan was reformatted into the accompanying digital, numerical time series by the Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO. Acknowledgement of SOI, NIO, and INCOIS should be included in any future scientific works using this record.	proprietary
@@ -15957,11 +15637,7 @@ doi:10.7289/V5C827KJ_Not Applicable	Archival and Discovery of August 27, 1883 Ts
 doi:10.7289/V5GX48VS_Not Applicable	Archival and Discovery of December 23, 1854 Tsunami Event on Marigrams	NOAA_NCEI STAC Catalog	1854-12-21	1854-12-27	-122.4375, 32.70059, -117.22565, 37.69944	https://cmr.earthdata.nasa.gov/search/concepts/C2105865663-NOAA_NCEI.umm_json	NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost.  As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access.  The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations.  All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.	proprietary
 doi:10.7289/V5TM78D3_Not Applicable	Archival and Discovery of March 2, 1933 Tsunami Event on Marigrams	NOAA_NCEI STAC Catalog	1933-02-26	1933-03-14	-157.85, 21.5, -117.22565, 48.545	https://cmr.earthdata.nasa.gov/search/concepts/C2105865671-NOAA_NCEI.umm_json	NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost.  As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access.  The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations.  All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.	proprietary
 doi:10.7289/V5X0657Z_Not Applicable	Archival and Discovery of March 28, 1964 Tsunami Event on Marigrams	NOAA_NCEI STAC Catalog	1964-03-22	1964-03-31	168.344824, -46.598233, -139.733444, 59.5485	https://cmr.earthdata.nasa.gov/search/concepts/C2105865674-NOAA_NCEI.umm_json	NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections.	proprietary
-drivers-of-the-microbial-metabolic-quotient-across-global-grasslands_1.0	Drivers of the microbial metabolic quotient across global grasslands	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	144.140625, -25.6309638, -148.359375, 65.4448709	https://cmr.earthdata.nasa.gov/search/concepts/C3226081473-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography    Please cite this paper together with the citation for the datafile.    The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (<1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.	proprietary
-drought-alters-c-footprint-of-trees-in-soil-13c-pulse-labelling-experiment_1.0	Drought alters C footprint of trees in soil: tracking the spatio-temporal fate of 13C labelled assimilates in the soil	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	7.5325012, 46.2542959, 7.6945496, 46.339691	https://cmr.earthdata.nasa.gov/search/concepts/C2789814585-ENVIDAT.umm_json	Data from pulse-labelling experiment with 100-year old trees of a naturally dry pine forest exposed to a 15-year-long irrigation experiment. Canopies of 10 trees were labelled for 3 hours with 13CO2 and the fate of this label was traced for one year in stem and soil respiration and in microbial biomass around these trees. Data include (1) microclimatic data and soil respiration rates of the year following pulse labelling. (2) Temporal patterns of the 13C signal and 13C excess in soil respired CO2 and microbial biomass. (3) Spatial distribution of 13C signal in the soil.	proprietary
-drought-and-beech-1000-beech-project_1.0	Data on multi-year drought impacts on European beech in northern Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.4761963, 47.2866819, 9.1351318, 47.8242201	https://cmr.earthdata.nasa.gov/search/concepts/C3226081555-ENVIDAT.umm_json	This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors  on  mortality  and  drought  legacy  processes.	proprietary
 dtms0bil_247_1	BOREAS Daedalus TMS Level-0 Imagery: Digital Counts in BIL Format	ORNL_CLOUD STAC Catalog	1994-09-16	1994-09-17	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2846959845-ORNL_CLOUD.umm_json	The level-0 Daedalus TMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.	proprietary
-dynamics-of-insect-natural-enemies-of-bark-beetles_1.0	Dynamics of insect natural enemies of bark beetles	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.1146183, 46.9811854, 9.1285229, 46.9903195	https://cmr.earthdata.nasa.gov/search/concepts/C2789814602-ENVIDAT.umm_json	In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse.  Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above.  The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting.	proprietary
 e1c0c34e0cc942898b3626efd1dcc095_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1	FEDEO STAC Catalog	2014-10-10	2017-03-17	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143580-FEDEO.umm_json	This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.	proprietary
 e2c223cdcb4844f9a1ffe9759b61eaf4_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 5.0	FEDEO STAC Catalog	1997-09-04	2020-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143024-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).	proprietary
 e2f9d8f61a02431997361a8827eaf558_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 5.0	FEDEO STAC Catalog	1997-09-04	2020-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142848-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note,  the IOP data are also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)	proprietary
@@ -15987,79 +15663,23 @@ early_iceberg_obs_1	Early Iceberg Observations by Australian Antarctic Expeditio
 echidna_1045_1	ECHIDNA LIDAR Campaigns: Forest Canopy Imagery and Field Data, U.S.A., 2007-2009	ORNL_CLOUD STAC Catalog	2007-08-01	2009-08-05	-119.25, 36.96, -68.72, 45.21	https://cmr.earthdata.nasa.gov/search/concepts/C2556025141-ORNL_CLOUD.umm_json	This data set contains forest canopy scan data from the Echidna? Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007 New England Campaign; 2008 Sierra National Forest Campaign; and 2009 New England Campaign. The New England field sites were located in Harvard Forest (Massachusetts), Howland Research Forest (Maine), and the Bartlett Experimental Forest (New Hampshire).The objective of the research was to evaluate the ability of the EVI ground-based, scanning near-infrared lidar to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, and other useful forest structural parameters rapidly and accurately.The EVI scan data are Andrieu Transpose (AT) Projection images in ENVI *.img and *.hdr file pairs. There are 28 images from the 2007 New England Campaign, 30 images from the 2008 Sierra National Forest Campaign, and 54 images from the 2009 New England Campaign. There are range-weighted mean preview image files (.jpg format) for each AT Projection image.Manual measurements of tree structural properties were made during each campaign at EVI scan locations. The field measurements are provided in one file for each campaign (.csv format). Parameters include species identification, DBH, tree height, crown base, etc. organized by field plot. There is also a data file (.csv format) which compares EVI derived measurements to the field measured data (DBH, stem density, basal area, biomass, and LAI) from the 2007 New England Campaign.	proprietary
 ecmwf2_523_1	BOREAS AFM-08 ECMWF Hourly Surface and Upper Air Data for the SSA and NSA	ORNL_CLOUD STAC Catalog	1994-05-13	1997-03-31	-114, 48, -92, 62	https://cmr.earthdata.nasa.gov/search/concepts/C2808093190-ORNL_CLOUD.umm_json	Hourly data from the ECMWF operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface, at Candle Lake, Saskatchewan, in the SSA and Thompson, Manitoba, in the NSA 13-May-1994 to 30-Sept-1994 and 01-Mar-1996 to 31-Mar-1997.	proprietary
 ecmwf_met_1deg_1222_1	ISLSCP II ECMWF Near-Surface Meteorology Parameters	ORNL_CLOUD STAC Catalog	1985-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784255955-ORNL_CLOUD.umm_json	This data set for the ISLSCP Initiative II data collection provides meteorology data with fixed, monthly, monthly-6-hourly, 6-hourly, and 3-hourly temporal resolutions. The data were derived from the European Centre for Medium-range Weather Forecasts (ECMWF) near-surface meteorology data set, 40-year re-analysis, or ERA-40 (Simmons and Gibson, 2000), which covers the years 1957 to 2001. The data were processed onto the ISLSCP II Earth grid with a spatial resolution of 1-degree in both latitude and longitude, and span the common ISLSCP II period from 1986 to 1995.The ECMWF forecast system is called the Integrated Forecasting System (IFS) and was developed in co-operation with Meteo-France. For ERA40 it is used with 60 levels from the top of the model at 10 Pa to the lowest level at about 10 m above the surface. There are 46 compressed (.tar.gz) data files with this data set. Each uncompressed file contains space-delimited text (.asc) data files.	proprietary
-ecological-properties-of-urban-ecosystems-biodiversity-dataset-of-zurich_1.0	Ecological properties of urban ecosystems. Biodiversity dataset of Zurich	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.4639359, 47.3297483, 8.6026382, 47.4276227	https://cmr.earthdata.nasa.gov/search/concepts/C2789814615-ENVIDAT.umm_json	Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.  Data are provided on request to contact person against bilateral agreement.	proprietary
-ecosystem-coupling-and-multifunctionality-exclosure-experiment_1.0	Ecosystem coupling and multifunctionality - exclosure experiment	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	10.0270844, 46.59481, 10.3951263, 46.7662842	https://cmr.earthdata.nasa.gov/search/concepts/C2789814632-ENVIDAT.umm_json	"This dataset contains all data on which the following publication below is based.    __Paper Citation:__   > Risch AC, Ochoa-Hueso R, van der Putten WH, Bump JK, Busse MD, Frey B, Gwiazdowicz DJ, Page-Dumroese DS, Vandegehuchte ML, Zimmermann S, Schütz M. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. 2018. Nature Communications 9: 3684.  [doi: 10.1038/s41467-018-06105-4](https://doi.org/10.1038/s41467-018-06105-4).    Please cite this paper together with the citation for the datafile.    #Methods  ##Study sites   The experimental exclosure setups were installed within the SNP (IUCN category Ia preserve; Dudley 2008), in south-eastern Switzerland. The park covers 172 km2 of forests and subalpine and alpine grasslands along with scattered rock outcrops and scree slopes. The entire area has been protected from human impact (no hunting, fishing, camping or off-trail hiking) since 1914. Large, fairly homogenous patches of short- and tall-grass vegetation, which originate from different historical management and grazing regimes, cover the park’s subalpine grasslands entirely. Short-grass vegetation developed in areas where cattle used to rest (nutrient input) prior to the park’s foundation (14th century to 1914) (Schütz and others 2003, 2006) and is dominated by lawn grass species such as Festuca rubra L., Briza media L. and Agrostis capillaris L. (Schütz and others 2003, 2006). Today, this vegetation type is intensively grazed by diverse vertebrate and invertebrate communities that inhabit the park and consume up to 60% of the available biomass (Risch and others 2013). Tall-grass vegetation developed where cattle formerly grazed, but did not rest, and is dominated by rather nutrient-poor tussocks of Carex sempervirens Vill. and Nardus stricta L. (Schütz and others 2003, 2006). This vegetation type receives considerably less grazing, with only roughly 20% of the biomass consumed (Risch and others 2013). Consequently, the two vegetation types together represent a long-term trajectory of changes in grazing regimes. Underlying bedrock of all grasslands is dolomite, which renders these grasslands rather poor in nutrients regardless of former and current land-use regimes.  ##Experimental design  To progressively exclude aboveground vertebrate and invertebrate animals, we established 18 size-selective exclosure setups (nine in short-grass, nine in tall-grass vegetation) distributed over six subalpine grasslands across the SNP (Risch and others 2013, 2015). Elevation differences of exclosure locations did not exceed 350 m (between 1975 and 2300 m a.s.l.). The exclosures were established immediately after snowmelt in spring 2009 and were left in place for five consecutive growing seasons (until end of 2013). They were, however, temporarily dismantled every fall (late October after first snowfall) to protect them from avalanches. They were re-established in the same location every spring immediately after snowmelt. Each size-selective exclosure setup consisted of five plots (2 x 3 m) that progressively excluded aboveground vertebrates and invertebrates from large to small. The plots are labelled according to the guilds that had access to them “L/M/S/I”, “M/S/I”, “S/I”, “I”, “None”; L = large mammals, M = medium mammals, S = small mammals, I = invertebrates, None = no animals had access. As we only had permission to have the experimental setup in place for five consecutive growing seasons, the experiment had to be completely dismantled in the late fall of 2013 and all material removed from the SNP.  Our exclosure design was aimed at excluding mammalian herbivores, but naturally also excluded the few medium and small mammalian predators, as well as the entire aboveground invertebrate food web. A total of 26 large to small mammal species can be found in the SNP, but large apex predators are missing (wolf, bear, lynx) . Reptiles, amphibians and birds are scarce to absent in the subalpine grasslands under study. Only two reptile species occur in the park and they are confined to rocky areas that warm up enough for them to survive. One frog species spawns in an isolated pond far from our grasslands. Only three bird species occasionally feed on the subalpine grasslands. Using game cameras (Moultrie 6MP Game Spy I-60 Infrared Digital Game Camera, Moultrie Feeders, Alabaster, AL, USA), we did observe that the medium- and small-sized mammals (marmot/hares and mice) were not afraid to enter the fences and feed on their designated plots. We never spotted reptiles, amphibians or birds on camera. We distinguished between 59 higher aboveground-dwelling invertebrate taxa that our size-selective exclosures excluded (see also methods for aboveground-dwelling invertebrates below).   The “L/M/S/I” plot (not fenced) was located at least 5 m from the 2.1 m tall and 7 x 9 m large main electrical fence that enclosed the other four plots. The bottom wire of this fence was mounted at 0.5 m height and was not electrified to enable safe access for medium and small mammals, while fencing out the large ones. Within each main fence, we randomly established four 2 x 3 m plots separated by 1-m wide walkways from one another and from the main fence line: 1) the “M/S/I” plots were unfenced, allowing access to all but the large mammals; 2) the “S/I” plots (10 x 10 cm electrical mesh fence) excluded all medium-sized mammals. Note that the bottom 10 cm of this fence remained non-electrified to enable safe access for small mammals; 3) the “I” plots (2 x 2 cm metal mesh fence) excluded all mammals. We double-folded the mesh at the bottom 50 cm to reduce the mesh size to smaller than 1 x 1 cm openings; and 4) the “None” plots were surrounded by a 1 m tall mosquito net (1.5 x 2 mm) to exclude all animals. The top of the plot was covered with a mosquito-meshed wooden frame mounted to the corner posts (roof). We treated these plots a few times with biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) to remove insects that might have entered during data collection or that hatched from the soil, but amounts were negligible and did not impact soil moisture conditions within these plots.  To assess whether the design of the “None” exclosure (mesh and roof) affected the response variables within the plots and, therefore, influenced the results, we established an additional six “micro-climate control” exclosures (one in each of the six grasslands) (Risch and others 2013, 2015). These exclosures were built as the “None” exclosures but were open at the bottom (20 cm) of the 3-m side of the fence facing away from the prevailing wind direction to allow invertebrates to enter. A 20-cm high and 3-m long strip of metal mesh was used to block access to small mammals. Thus, this construction allowed a comparable micro-climate to the “None” plots, but also a comparable feeding pressure by invertebrates to the “I” plots. We compared various properties within these exclosures against one another to assess if our construction altered the conditions in the “None” plots. We showed that differences in plant (e.g., vegetation height, aboveground biomass) and soil properties (e.g., soil temperature, moisture) found between the “I” and the “None” treatments were not due to the construction of the “None” exclosure, but a function of animal exclusions, although the amount of UV light reaching the plant canopy was significantly reduced (Risch and others 2013).   ##Aboveground invertebrate sampling  Aboveground invertebrates were sampled with two different methods to capture both ground- and plant-dwelling organisms: 1) we randomly placed two pitfall traps (67 mm in diameter, covered with a roof) filled with 20% propylene glycol in one 1 x 1 m subplot of the 2 x 3 m treatment plots in spring 2013 (May) and emptied them every two weeks until late September 2013 (Vandegehuchte and others 2017b, 2018). A pitfall trap consisted of a plastic cylinder (13 cm depth, 6.75 cm diameter). Within each cylinder we placed a 100 ml plastic vial with outer diameter 6.70 cm and on top of the cylinder we placed a plastic funnel to guide the invertebrates into the vials. Each trap was cover with a cone-shaped and transparent plastic roof to protect the trap from rain (Vandegehuchte and others 2017b, 2018). Note that in the “None” plots only one trap was placed as control to check for effectiveness of the exclosure. 2) We vacuumed all invertebrates from a 60 x 60 cm area on another 1 x 1 m subplot with a suction sampler (Vortis, Burkhard manufacturing CO, Ltd., Rickmansworth, Hertfordshire, UK) every month from June to September 2013 (Vandegehuchte and others 2017b, 2018). For this purpose, we quickly placed a square plastic frame (60 x 60 x 40 cm) with a closable mosquito mesh sleeve attached to the top edge into the plot from the outside. The suction sample was then inserted into through the sleeve and operated for 45 s to collect the invertebrates (Vandegehuchte and others 2017b, 2018).   We sorted the ≈100 000 individuals collected with both methods by hand and identified each individual morphologically to the lowest taxonomic level feasible (59 taxa, including orders, suborders, subfamilies, families; phylum for Mollusca). These taxa belonged to the following feeding types: 19 herbivores, 16 detritivores, 9 predators, 8 mixed feeders, 5 omnivores and 2 non-classified feeders (or not feeding as adults) (Vandegehuchte and others 2017b). We summed the numbers from the two pitfall traps and the suction sampling over the course of the 2013 season to represent the aboveground invertebrate abundance and community composition of a plot. Note: we did not specifically attempt to catch flying invertebrates with e.g., sticky traps, thus a few flying insects may have been missed with our vacuum sampling approach.  ##Sampling of plant properties   The vascular plant species composition was assessed at peak biomass every summer (July) by estimating the frequency of occurrence of each species with the pin count method in each plot (Frank and McNaughton 1990). A total of 172 taxa occurred within our 90 plots and we calculated plant species richness for each plot separately. We used the 2013 data in this study. Plant quality was assessed every year in July and September; here we use plant quality at the end of the experiment (September 2013). Two 10 x 100 cm wide strips of vegetation per plot were clipped, combined, dried at 65°C, and ground (Pulverisette 16, Fritsch, Idar-Oberstein, Germany) to pass through a 0.5 mm sieve. Twenty randomly selected samples across all treatments were analysed for N (Leco TruSpec Analyser, Leco, St. Joseph, Michigan, USA) (Vandegehuchte and others 2015). Nitrogen concentrations of the other samples were then estimated from models established for the experiment and the entire SNP relating Fourier transform-near infrared reflectance (FT-NIR) spectra to the measured values of N using a multi-purpose FT-NIR spectrometer (Bruker Optics, Fällanden, Switzerland) (Vandegehuchte and others 2015). Root biomass was sampled every fall by collecting five 2.2 cm diameter x 10 cm deep soil samples (Giddings Machine Company, Windsor, CO, USA) per plot (450 samples year-1). The samples were dried at 30 °C and roots were sorted from the sample by hand. We sorted each sample for 1 h which allowed to retrieve over 90% of all roots present in the samples (Risch and others 2013). The roots were then dried at 65 °C for 48 and weighed to the nearest mg. We averaged the values per plot and used the 2013 data only in this study.  ##Sampling of edaphic communities  In 2009, 2010, and 2011 we collected three composited soil samples (5 cm diameter x 10 cm depth; AMS Samplers, American Falls, ID, USA) and assessed bacterial community structure using T-RFLP profiling (Liu and others 1997; Blackwood and others 2003; Hodel and others 2014). We detected a total of 89 operational taxonomic units (OTUs). These values are in accordance with other studies reporting OTU richness (Wirthner and others 2011; Zumsteg and others 2012; Meola and others 2014) using T-RFLP profiling, a method that detects the most abundant, and thus likely, the most relevant, taxa. We averaged the data over the three years of collections for our calculations. Microbial biomass carbon (MBC) was determined with the substrate-induced method (Anderson and Domsch 1978) every fall (September) between 2009 and 2013 by collecting three mineral soil samples (5 cm diameter × 10 cm mineral soil core, AMS Samplers, American Falls, ID, USA). The three samples were combined (90 samples for each sampling year), immediately put on ice, taken to the laboratory, passed through a 2-mm sieve and stored at 4°C. Again, we only used the 2013 data in this study.  Soil samples (5 cm diameter x 10 cm depth) to extract soil arthropods were collected in June, July, and August 2011 with a soil corer lined with a plastic sleeve to ensure an undisturbed sample (total of 270 samples). The plastic line core was immediately sealed on both ends using cling film and put into a cooler. All plots were sampled within three days and the extraction of arthropods started the evening of the sampling day using a high-gradient Tullgren funnel apparatus (Crossley and Blair 1991; Vandegehuchte and others 2015). Samples were kept in the extractor for four days and the soil arthropods were collected in 95% ethanol. All individuals were counted and each individual was identified morphologically to the lowest level feasible [76 taxa, including orders, suborders, subfamilies, families (Protura, Thysanoptera, Aphidina, Psylina, Coleoptera, Brachycera, Nematocera, Auchenorryncha, Heteroptera, Formicidae); sub-phylum for Myriapoda, for Acari and Collembola also including morpho-species). Note that we also included larval stages (nine of the 76 taxa) (Vandegehuchte and others 2015). All data were summed over the season. A detailed species list for mites and collembolans is published (Vandegehuchte and others 2017a) [https://doi.org/10.1371/journal.pone.0118679.s001]. Earthworms are rare in the SNP and therefore were not included. We collected eight random 2.2 cm diameter x 10 cm deep soil cores from each plot in September 2013 to determine the soil nematode community composition. The samples were mixed and the nematodes were extracted from 100 ml of fresh soil using Oostenbrink elutriators (Oostenbrink 1960). All nematodes in a 1 ml of the 10 ml extract were counted, a minimum of 150 individuals sample-1 were identified to genus or family level using (Bongers 1988), the numbers of all nematodes were extrapolated to the entire sample and expressed for a 100 g dry sample. In total we identified 63 genus or family levels (Vandegehuchte and others 2015). The list of all the nematodes found is published (Vandegehuchte and others 2015) [http://www.oikosjournal.org/appendix/oik-03341] or DOI: [doi: 10.1111/oik.03341].   We are aware that sampling soil microbes from 2009 to 2011 and soil arthropods in 2011 was not ideal, but we are positive that this does not bias the results. Most of the parameters measured in our experiment either already showed a treatment response after the first growing season (e.g., plant biomass) or did not respond over the entire time experiment (e.g., microbial biomass C). The microbial community composition (2009 – 2011) was highly influenced by inter-annual differences in temperature and precipitation, but did not differ between treatments or vegetation types (Hodel and others 2014). We therefore felt comfortable using the 2009 through 2011 data for describing the soil microbial community in our experimental treatments. Similarly, we are positive that our soil arthropod data are representative. We did assess soil arthropods in August 2012 and found no differences to the August 2011 data. However, we did not feel comfortable combining the 2011 June, July, August data with only August data for 2012 for our analyses.   ##Sampling of soil properties  We collected three soil samples (5 cm diameter x 10 cm depth) in each plot in September 2013 after removing the vegetation. First, we collected the top layer of mineral soil rich in organic matter, the surface organic layer or rhizosphere, typically 1 to 3 cm in depth with a soil corer (AMS Samples, American Falls, Idaho, USA). Second, we collected a 10 cm mineral soil core beneath this surface layer. The cores for each layer were composited, dried at 65 °C for 48 h and fine-ground to pass a 0.5 mm screen. We then analysed all samples for total C using a Leco TruSpec Analyser (Leco, St. Joseph, Michigan, USA). Mineral soil pH was measured potentiometrically in 1:2 soil:CaCl2 solution with an equilibration time of 30 min.   Soil net N mineralisation was assessed during the 2013 growing season (Risch and others 2015). For this purpose, we randomly collected a 5 cm diameter x10 cm deep soil sample with a soil corer (AMS Samples, American Falls, Idaho, USA) after clipping the vegetation in June 2013. After weighing and sieving (4 mm mesh) the soil, we extracted a 20 g subsample in 1 mol l-1 KCl for 1.5 h on an end-over-end shaker and thereafter filtered it through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnenmühle FineArt GmbH, Dassel, Germany). From these filtrates NO3- concentrations were measured colorimetrically (Norman and Stucki 1981) and NH4+with flow injection analysis (FIAS 300, Perkin Elmer, Waltham Massachusetts, USA) (Risch and others 2015). We dried the rest of the sample 105 °C to constant mass to determine fine,fraction bulk density. A second soil sample was collected within each plot in June 2013 with a corer lined with a 5 x 13 cm aluminium cylinder. The corer was driven 11.5 cm deep into the soil so that the top 1.5 cm of the cylinder remained empty. Into this space we placed a polyester bag (250 µm) filled an ion-exchanger resin to capture the incoming N. The bag was filled with a 1:1 mixture of acidic and alkaline exchanger resin (ion-exchanger I KA/ion exchanger IIIAA, Merck AG, Darmstadt, Germany). We then removed 1.5 cm soil at the bottom of the cylinder and placed a second resin exchanger bag into this space to capture the N leached from the soil column. To assure that the exchange resin was saturated with H+ and Cl- prior to filling the bags, the mixture was stirred with 1.2 ml l-1 HCl for 1 h and then rinsed with demineralized water until the electrical conductivity of the water reached 5 µm cm-1. The cylinder with the resin bags in place was reinserted into the soil with the top flush to the soil surface and incubated for three months. We recollected the cylinders in September 2013. Each resin bag and 20 g of sieved soil (4 mm mesh) from each cylinder were then separately extracted with KCl and NO3- and NH4+ concentrations were measured. Nitrate and NH4+ concentrations of all samples were then converted to a content basis by multiplying their values with fine fraction bulk density. Net N mineralisation was thereafter calculated as the difference between the N content of the samples collected at the end of the three-month incubation (including the N extracted from the bottom resin bag) and the N content at the beginning of the incubation (Risch and others 2015).   Soil CO2 emissions were measured every two weeks between 0900 and 1700 hrs from early May through late September 2013 with a PP-Systems SRC-1 soil respiration chamber (15 cm high, 10 cm diameter; closed circuit) attached to a PP-Systems EGM-4 infrared gas analyser (PP-Systems, Amesbury, MA, USA) on two locations per plot (Risch and others 2013). The chamber was placed on randomly placed, permanently installed PVC collars (10 cm diameter) driven 5 cm into the soil at the beginning of the study (Risch and others 2013). Freshly germinated plants growing within the collars were removed prior to each measurement to avoid measuring plant respiration or photosynthesis. The two measurements collected per plot and sampling date were averaged.   Soil moisture (with time domain reflectometry; Field-Scout TDR-100, Spectrum Technologies, Plainfield, Illionois, USA) and temperature (with a waterproof digital pocket thermometer; Barnstead International, Dubuque, Iowa, USA) were measured at five random locations per plot every two weeks during the growing seasons during the experiment for the 0 to 10 cm depth (Risch and others 2013, 2015). As soil moisture and soil temperature were highly negatively correlated (Risch and others 2013), we only used soil moisture for this study. We used plot-level averages of all values available to capture soil moisture variability during the five years of the experiment. The results remained unchanged when we only used soil moisture from the 2013 growing season.  ##Numeral calculations and statistical analyses  Ecosystem coupling. We conducted principal component analyses (PCAs; unscaled) at the complete dataset level using the abundances of each taxonomical entity to describe each of the five different communities used in this study: aboveground-dwelling invertebrates, vascular plants, soil microorganisms, soil arthropods and soil nematodes. We retained the first two components (PCA axis 1 and PCA axis 2) of each analysis as we found them to adequately represent the temporal and spatial variability of our 90 treatment plots in previous studies55,67. Together they explained a total of 71.70% of the variation for aboveground invertebrates, 44.36% for plants, 44.85% for soil microorganisms, 61.85% for soil arthropods and 77.19% for soil nematodes. In addition, we used soil pH and soil organic C content as a proxy for soil chemical properties, soil bulk density as a proxy for soil physical properties and soil moisture (negatively correlated with soil temperature) as a proxy for soil micro-climatic conditions for an overall total of fourteen constituents.  We calculated ecosystem coupling9 for each exclosure treatment within each vegetation type (i.e., 2  5 treatment combinations in total) as an integrated measure of pairwise ecological interactions between ecosystem constituents representing ecological communities and the soil abiotic environment. These ecological interactions are defined by non-parametric Spearman rank correlation analyses between two constituents, excluding interactions involving two abiotic constituents (e.g., soil pH vs. soil moisture) and interactions between the first (PC1) and second (PC2) component of each community type, as these are orthogonal by definition. Interactions between abiotic constituents were excluded from the analyses because the focus of our study was on communities and how they interact with one another and their surrounding environment; therefore, including abiotic-abiotic interactions was not of interest here. Given that the effectiveness of our experimental design resulted in that no community composition data of aboveground-dwelling invertebrates was available for the “None” plots (all animals excluded), only thirteen instead of fourteen constituents were included in the ecosystem coupling calculations for this treatment. The complete absence of aboveground invertebrates represents the most extreme case of disturbance between aboveground animal communities and the rest of the ecosystem constituents. This may have resulted in a slight overestimation of ecosystem coupling for these plots.   	Average ecosystem coupling was calculated as follows:   Ecosystem coupling=  where Xi is the absolute Coupling was calculated value of the Spearman’s rho coefficient of the ith correlation for each treatment within each vegetation type (i.e., based on nine replicates each), considering and n is the number of pairwise comparisons considered (n = a total of 80;  interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).	Coupling was calculated for each treatment within each vegetation type (i.e., based on nine replicates each), considering a total of 80 interactions (56 in the case of the “None” treatment). We considered a total of 40 biotic-biotic interactions (i.e., concerning two community-level principal components such as plants and microbes; 24 in the case of the “None” treatment) and 40 abiotic-biotic (i.e., concerning one community-level principal component and one abiotic factor, e.g., plant community and soil properties; 32 in the case of the “None” treatment).   To establish whether constituents were significantly and positively coupled within treatments (i.e., the average of their correlation coefficients were greater than in a null model where correlation only happens by chance), we calculated one-tailed p-values based on permutation tests with 999 permutations.        We considered six ecosystem functions and process rates commonly used to assess ecosystem functioning (Meyer and others 2015; Manning and others 2018). Plant N content represents a measure of forage quality, while plant richness has been shown to stabilise biomass production, thus allowing the system to respond to changes in herbivory. Soil net N mineralisation, soil respiration, root biomass, and microbial biomass represent fluxes or stocks of energy. For all functions and processes higher values represent higher functioning (Manning and others 2018). All these variables were measured in the last year of the experiment (2013). We then quantified ecosystem multifunctionality using the multiple threshold approach (Byrnes and others 2014; Manning and others 2018), which considers the number of functions that are above a certain threshold, over a series of threshold values (typically 10-99%) that are defined based on the maximum value of each function. We weighted all our functions equally for these calculations (Manning and others 2018). The number of functions in a plot with values higher than a given threshold value for the respective function is summed up. The sum represents ecosystem multifunctionality for that plot. Given that choosing any particular threshold as a measure of ecosystem multifunctionality is arbitrary, we calculated the average of thresholds from 10-90% (in 10% intervals) as a more integrated representation of ecosystem multifunctionality.   We used Pearson correlations to explore the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and ecosystem multifunctionality by calculating the slopes of all relationships between ecosystem coupling and multifunctionality for all thresholds between 10 and 99%. We also related ecosystem coupling with the average of multifunctionality at thresholds between 30-80% as explained before and considered this correlation as a robust indication of the type of association between these two variables. In addition, we explored the relationships between ecosystem coupling (all interactions, biotic-biotic interactions, abiotic-biotic interactions involving above- and belowground constituents, and all interactions, biotic-biotic interactions, abiotic-biotic interactions involving belowground constituents only) and individual ecosystem functions. The effects of exclosures and vegetation type on individual functions and multifunctionality were evaluated using linear mixed effects models ('lme' function of the nlme package), with exclosure and vegetation type as fixed effects and fence as a random factor. All statistical analyses and numerical calculations were done in R version 3.4.0 (R Core Team 2016).    #References    - Anderson J, Domsch K. 1978. A physiological method for the quantitative measurement of microbial biomass in soil. Soil Biol Biochem 10:215–21.  - Blackwood CB, Marsh T, Kim S-H, Paul EA. 2003. 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For Ecol Manage 181:177–88.  - Vandegehuchte ML, van der Putten WH, Duyts H, Schütz M, Risch AC. 2017a. Aboveground mammal and invertebrate exclusions cause consistent changes in soil food webs of two subalpine grassland types, but mechanisms are system-specific. Oikos 126:212–23.  - Vandegehuchte ML, Raschein U, Schütz M, Gwiazdowicz DJ, Risch AC. 2015. Indirect short- and long-term effects of aboveground invertebrate and vertebrate herbivores on soil microarthropod communities. PLoS One 10:e0118679.  - Vandegehuchte ML, Schütz M, de Schaetzen F, Risch AC. 2017b. Mammal-induced trophic cascades in invertebrate food webs are modulated by grazing intensity in subalpine grassland. J Anim Ecol 86:1434–46.  - Vandegehuchte ML, Trivellone V, Schütz M, Firn J, de Schaetzen F, Risch AC. 2018. Mammalian herbivores affect leafhoppers associated with specific plant functional types at different timescales. Funct Ecol 32:545–55.  - Wirthner S, Frey B, Busse MD, Schütz M, Risch AC. 2011. Effects of wild boar (Sus scrofa L.) rooting on the bacterial community structure in mixed-hardwood forest soils in Switzerland. Eur J Soil Biol 47:296–302. http://dx.doi.org/10.1016/j.ejsobi.2011.07.003  - Zumsteg A, Luster J, Göransson H, Smittenberg RH, Brunner I, Bernasconi SM, Zeyer J, Frey B. 2012. Bacterial, Archaeal and Fungal Succession in the Forefield of a Receding Glacier. Microb Ecol 63:552–64. https://doi.org/10.1007/s00248-011-9991-8"	proprietary
 ecosystem_roots_1deg_929_1	ISLSCP II Ecosystem Rooting Depths	ORNL_CLOUD STAC Catalog	1995-02-01	1995-07-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784847849-ORNL_CLOUD.umm_json	The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths.The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells. Related data sets: The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003).This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.	proprietary
 ecousm1_Not provided	A comparative study on floral ecology between Malaysia and Antarctica	SCIOPS STAC Catalog	1970-01-01		110.32, -66.28, 110.32, -66.28	https://cmr.earthdata.nasa.gov/search/concepts/C1214621680-SCIOPS.umm_json	The major objectives of this project are as follows:  1. To determine the composition and distribution of algal flora from a wide range of habitats, which provide a conductive niche for algal population in Antarctica. 2. To compare the Antarctic and tropical algal flora, in order to determine the degree of species endemism based on evolutionary process. 3. To study the important role of habitat specificity in determining the composition of diatom assemblages. 4. To test the utility and suitability of diatom community structure as indicators of environmental stress.   This is done by: 1. Conducting an ecological survey of microalgal distribution at Australian Antarctic station sites by looking into several types of habitat. 2. Identifying the microalgae samples collected based on morphology using light microscopy and SEM. 3. Comparing the algae community, structure and distribution from the tropics.   The principal milestones of the project are as follows: 1. Information of microalgal distribution at several sites in Antarctica. 2. Collection of microalgae cultures.  3. Completion of identification of Antarctic microalgae.  In collaboration with the Australian Antarctic Division (AAD) we have gone on an expeditions to Australian Antarctic Station of Casey and Davis. Collection of samples was made from various sources such as  water, snow and soil and we have established a list of microalgae species in our collection. Comparative studies on the species diversity and distribution with tropical microalgae communities are being conducted. Physiological studies are currently in progress.	proprietary
-ect-and-rb-data-switzerland_1.0	ECT and RB data Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	6.6500243, 45.8050626, 10.5831297, 47.4867706	https://cmr.earthdata.nasa.gov/search/concepts/C2789814654-ENVIDAT.umm_json	"The data set contains the data used in the publication ""On snow stability interpretation of Extended Column Test results"" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences."	proprietary
 edaa7e7324e849f683d3726088a0c7bd_NA	ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 3.1	FEDEO STAC Catalog	1997-09-03	2016-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142506-FEDEO.umm_json	The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains their Version 3.1 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites).   Note, this the IOP data is also included in the 'All Products' dataset. The inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.   (A separate dataset is also available for data on a sinusoidal projection.)	proprietary
 edc_landcover_xdeg_930_1	ISLSCP II IGBP DISCover and SiB Land Cover, 1992-1993	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784854847-ORNL_CLOUD.umm_json	This data set describes the geographic distributions of 17 classes of land cover based on the International Geosphere-Biosphere DISCover land cover legend (Loveland and Belward 1997) and the 15 classes of the SiB model processed at the USGS EROS Data Center (EDC). Specifically, the resampled DISCover datasets were derived from the 1km DISCover data set compiled by the USGS. The 1km data sets for each classification scheme were aggregated to 1, 0.5 and 0.25 degree spatial resolutions for this ISLSCP II data collection. Each layer of the aggregated products corresponds to a single DISCover land cover category and the values represent the percentage of the coarse resolution cell (1 degree, etc...)occupied by that land cover category. The dominant class data show the land cover category that occupies the majority of the cell and is derived from the percentage files for each cover type. The objective of this study was to create a land cover map derived from 1 kilometer AVHRR data using a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. During this re-processing, the original EDC land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by global modelers and others. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.	proprietary
 edgar_atmos_emissions_1deg_1022_1	ISLSCP II EDGAR 3 Gridded Greenhouse and Ozone Precursor Gas Emissions	ORNL_CLOUD STAC Catalog	1970-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785350291-ORNL_CLOUD.umm_json	The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 *.zip data files with this data set.	proprietary
-edna-fjord-svalbard-fish-plankton_1.0	Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	10.645752, 78.9769189, 12.689209, 79.4215522	https://cmr.earthdata.nasa.gov/search/concepts/C3226081772-ENVIDAT.umm_json	This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).     # Methods       **Sampling**  We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.      **Molecular**  To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).     # Data content:     + Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.   + Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.       # Additional informations  For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.	proprietary
 edward_viii_sat_1	Edward VIII Gulf Satellite Image Map 1:100 000	AU_AADC STAC Catalog	1993-11-01	1993-11-30	56, -68, 58, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214308437-AU_AADC.umm_json	Satellite image map of Edward VIII Gulf, Kemp Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1993.  The map is at a scale of 1:100000, and was produced from a Landsat TM (WRS 139-107) scene (bands 2,3 and 4). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and penguin colonies, and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
-eemma_1.0	eemma.R, an R script for Ensemble End-Member Mixing Analysis	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226081791-ENVIDAT.umm_json	The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.    A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.    The user's guide is available for public use under Creative Commons CC-BY-SA.  Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/	proprietary
 ef1627f523764eae8bbb6b81bf1f7a0a_NA	ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 1.1	FEDEO STAC Catalog	1992-09-15	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142828-FEDEO.umm_json	"This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and   functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:•	Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.•	Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.•	Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. •	Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. •	Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents."	proprietary
 ef5c6596cae548c6aea9dea181c7624c_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1	FEDEO STAC Catalog	2014-10-09	2017-03-17	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143017-FEDEO.umm_json	This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid.	proprietary
 ef6a9266-a210-4431-a4af-06cec4274726_NA	Cartosat-1 (IRS-P5) - Panchromatic Images (PAN) - Europe, Monographic	FEDEO STAC Catalog	2015-02-10		-25, 30, 45, 80	https://cmr.earthdata.nasa.gov/search/concepts/C2207457985-FEDEO.umm_json	Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data.	proprietary
 ef8eb5ff84994f2ca416dbb2df7f72c7_NA	ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2019), version 1.0	FEDEO STAC Catalog	2000-02-24	2019-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143047-FEDEO.umm_json	This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.  Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 – 2019. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.	proprietary
-effective_anisotropic_elasticity_tensor_of_snow_firn_and_bubbly_ice_1.0	Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.8225713, 46.796135, 9.8225713, 46.796135	https://cmr.earthdata.nasa.gov/search/concepts/C3226081817-ENVIDAT.umm_json	The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.  The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.  We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy. This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example. Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.	proprietary
-effects-of-canopy-disturbance-on-swiss-forests_1.0	Effects of canopy disturbance on Swiss forests	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814791-ENVIDAT.umm_json	"The files refer to the data used in Scherrer et al. (2021) ""Canopy disturbances catalyse tree species shifts in Swiss forests"" in _Ecosystems_.    The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory.  In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication.    For more detailed information about the data files please check the ReadMe.docx file."	proprietary
 elev_arc_250_1	BOREAS Elevation Contours over the NSA and SSA ARC/Info Generate Format	ORNL_CLOUD STAC Catalog	1970-01-01	1989-12-31	-105.23, 53.69, -98.09, 56.06	https://cmr.earthdata.nasa.gov/search/concepts/C2846961083-ORNL_CLOUD.umm_json	Elevation contours over the NSA and SSA in ARC/Info Generate Format.  Data cover portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) and are on a scale of 1:50,000.	proprietary
-elevation-profiler-first-release_1.0	elevation-profiler: first release	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	8.4545978, 47.3606372, 8.4545978, 47.3606372	https://cmr.earthdata.nasa.gov/search/concepts/C3226081954-ENVIDAT.umm_json	Elevation profiler (see Krebs et al. 2015) is an open source GIS tool designed to work with ArcGIS that automatically calculates transverse or longitudinal elevation profiles of different lengths starting from a digital elevation model (e.g. high resolution Lidar DEM) and a shapefile of points (i.e. the midpoints of the profile segments). The calculated profiles are then saved in comma-separated tabular data files (.csv).    GNU General Public License v2.0 only	proprietary
-elk-and-bison-carcasses-in-yellowstone-usa_1.0	Elk and bison carcasses in Yellowstone, USA	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789814899-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology   doi: ...    Methods  Study area and study sites  This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.   For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).   The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).     Ecosystem functions and soil properties  We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).  Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.   Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).  The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.  In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.    Statistical analyses  Univariate analyses for ecosystem functions, soil biotic and abiotic properties  We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.  We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).   Multivariate analyses  Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).   Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).    References  Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x  Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399  Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073  Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.  Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.  Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0  Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.  De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.  De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x  Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.  Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401  Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11  Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.  Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028  Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.  Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545  Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.  Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.  Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.  Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.  Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.  Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4  Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7  Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.  Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.  Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219  Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.  R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.  R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.  Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051  Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x  Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09  Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.  Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07  Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.      Please cite this paper together with the citation for the datafile.	proprietary
 em_database_1	Electron Microscope Database	AU_AADC STAC Catalog	1983-01-01		20, -90, 180, -40	https://cmr.earthdata.nasa.gov/search/concepts/C1214308438-AU_AADC.umm_json	This database contains information pertaining to the negatives taken by the laboratory since its inception.  Both scanning and transmission electron micrographs are catalogued within this database.  Among other things, the database includes a large number of images of protists.  The URLs provided link to a marine specimens database, and a terrestrial and limnetic specimens database.	proprietary
-emergence-dynamics-of-natural-enemies_1.0	Emergence dynamics of natural enemies of spruce bark beetles	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.2987976, 47.0713378, 8.9689636, 47.3846826	https://cmr.earthdata.nasa.gov/search/concepts/C2789814953-ENVIDAT.umm_json	In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.	proprietary
 enderby_flight_logs_1977_1	Enderby Land Flight Logs For Ice Radar and Navigation, 1977	AU_AADC STAC Catalog	1977-01-01	1977-01-28	54, -69, 63, -68	https://cmr.earthdata.nasa.gov/search/concepts/C1214308542-AU_AADC.umm_json	A series of flights over Enderby Land were carried out in January 1977 with an airborne ice radar. The flight notes on navigation, radar settings and other notes from the flight have been archived at the Australian Antarctic Division.	proprietary
 enderby_flight_logs_1979_1	Enderby Land Flight Logs For Ice Radar and Navigation, 1979/80	AU_AADC STAC Catalog	1979-12-12	1980-01-29	54, -69, 63, -68	https://cmr.earthdata.nasa.gov/search/concepts/C1214313463-AU_AADC.umm_json	A series of flights over Enderby Land were carried out in December 1979 and January 1980 with an airborne ice radar. The flight notes on navigation, radar settings and other notes from the flight have been archived in the records store at the Australian Antarctic Division.	proprietary
 enderby_land_grav_snow_1975_1	Gravity and Snow Accumulation, Enderby Land 1975-76	AU_AADC STAC Catalog	1975-01-01	1976-12-31	40, -75, 60, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214308543-AU_AADC.umm_json	Logs recording gravity and snow accumulation in Enderby Land in 1975-76.  Copies of these documents have been archived in the records store of the Australian Antarctic Division.	proprietary
 enderby_land_logs_1	Enderby Land Logbooks, 1972-1980	AU_AADC STAC Catalog	1972-01-01	1980-12-31	40, -75, 70, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214308560-AU_AADC.umm_json	Log books from field wok carried out in Enderby Land between 1972 and 1980. Information recorded includes borehole temperatures, ice movement, gravity, ice radar notes, and barometric pressure.  Physical copies of these documents have been stored in the Australian Antarctic Division records store.	proprietary
 enderby_reports_1978_1	Enderby Land Field Program Daily Logs and Report, 1977/78	AU_AADC STAC Catalog	1977-12-21	1978-02-15	54, -69, 63, -68	https://cmr.earthdata.nasa.gov/search/concepts/C1214308561-AU_AADC.umm_json	In 1977/78 ANARE carried out a summer operation that was a continuation of a multi-year project in the Enderby Land region, commenced in the 1974/75 season. Programs for 1977/78 included survey, high level photography, geochronolgy, structural geology, petrology, geophysics and glaciology. The programs were air supported from a field base near Mt. King (67 degrees 04'S, 52 degrees 52'E).  Planning and daily logbooks for the program, as well as the end of season report, have been archived at the Australian Antarctic Division.	proprietary
-endsplit_1.0	EndSplit	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-71.7920494, 43.9260264, -71.685276, 43.9638472	https://cmr.earthdata.nasa.gov/search/concepts/C2789814993-ENVIDAT.umm_json	"R scripts and demonstration data for end-member mixing and splitting: using isotopes and other tracers to determine where streamflow comes from (end-member mixing) and where precipitation goes (end-member splitting).      # This package includes two R scripts: #    ""EndSplit_v1.0_20200516.R"" implements end-member mixing and splitting.   ""EndSplit_demo_v1.0_20200516.R"" demonstrates the application of EndSplit to the Hubbard Brook Watershed 3 isotope data set (see below).    Both of these scripts are copyright (C) 2020 ETH Zurich and James Kirchner.  Public use is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/    But… READ THIS CAREFULLY:  ETH Zurich and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.    These scripts implement end-member mixing and end-member splitting, as described in Kirchner and Allen, ""Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis"", Hydrology and Earth System Sciences, 24, 17-39, https://doi.org/10.5194/hess-24-17-2020, 2020.  Users publishing results based on these scripts should cite that paper.    Build 2020.05.16 is a minor bug fix of build 2019.10.25, which was previously released as EndSplit_v1.0_20191025.R.    # The zip file ""demonstration input data.zip"" contains 8 demonstration data files (all tab-delimited text): #    ""Hubbard Brook WS3 isotope data split by sampling date.txt"" contains streamflow and precipitation isotope data from Hubbard Brook Watershed 3 isotope data (Campbell and Green, 2019).  ""Hubbard Brook WS3 daily P and Q 1956-2014.txt"" contains daily precipitation and streamflow totals for Hubbard Brook Watershed 3.  (USDA Forest Service Northern Research Station, 2016a and 2016b).    ""Hubbard Brook WS3 isotope data WY2007.txt"", ""Hubbard Brook WS3 isotope data WY2008.txt"", ""Hubbard Brook WS3 isotope data WY2009.txt"", ""Hubbard Brook WS3 daily P and Q WY2007.txt"", ""Hubbard Brook WS3 daily P and Q WY2008.txt"", and ""Hubbard Brook WS3 daily P and Q WY2009.txt"" contain subsets of these data for the designated water years.      As the work product of US federal employees, the data in these files are in the public domain.  However, any users of these data should cite the original sources:    Campbell, J. L., and Green, M. B.: Water isotope samples from Watershed 3 at Hubbard Brook Experimental Forest, 2006-2010, https://doi.org/10.6073/pasta/f5740876b68ec42b695c39d8ad790cee, 2019.    USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Daily Streamflow by Watershed, 1956 - present, https://doi.org/10.6073/pasta/38b11ee7531f6467bf59b6f7a4d9012b, 2016a.    USDA Forest Service Northern Research Station: Hubbard Brook Experimental Forest (US Forest Service): Total Daily Precipitation by Watershed, 1956 - present, https://doi.org/10.6073/pasta/163e416fb108862dc6eb857360fa9c90, 2016b.      # The zip file ""demonstration output files.zip"" contains demonstration output files  #    These tab-delimited text files were generated by running EndSplit_demo_v1.0_20200516.R (which in turn calls EndSplit_v1.0_20200516.R) under R version 3.6.0, using the input files contained in ""demonstration input data.zip"""	proprietary
-energy-cooperatives-in-switzerland-survey-results_1.0	Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082125-ENVIDAT.umm_json	"## Topic of Survey  The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project ""Collective financing of renewable energy projects in Switzerland and Germany"" of the National Research Programme 71 ""Managing Energy Consumption"". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.    ## Survey Method  The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software ""Sawtooth"". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by ""missing data"".     # Anonymization  Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.    # How to Use the Data  * The data are available in CSV and SPSS (sav.) format.  * A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.     # Current Embargo on Data  These data are currently under embargo and will only be released when the project is completed (not before 2020).    #Additional Information  * The used questionnaire is provided in German and French.   * Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943"	proprietary
-ensemble-hydrograph-separation_1.4	Ensemble hydrograph separation scripts	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815032-ENVIDAT.umm_json	Calculation scripts that perform ensemble hydrograph separation.  Identical scripts in R and MATLAB are provided, along with demonstration input time series and the corresponding outputs.  These scripts were tested on R version 3.6.2 (2019-12-12) and on MATLAB versions 2018b and 2109b.      These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/.    ETH Zurich, WSL, James Kirchner, and Julia Knapp make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose.  Users are solely responsible for determining the suitability and reliability of this software for their own purposes.	proprietary
-envidat-lwf-12_2019-03-06	Meteorological measurements LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 45.86141, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815107-ENVIDAT.umm_json	Continuous measurement of air temperature, relative humidity, wind speed and direction, global radiation, photosynthetic active radiation, UVB radiation, and precipation in an open field very close to the LWF plot as well as air temperature, relative humidity, wind speed, photosynthetic active radiation, and precipitation in the forest below the canopy.    ### Purpose: ###    Recording meteorological conditions        ### Manual Citation: ###     * Martine Rebetez, Gustav Schneiter, 1997: Meteorologie. In: Brang P. (ed.) Aufnahmeanleitung LWF. Langfristige Waldökosystem-Forschung LWF, 4 S.      *  Raspe  S,  Beuker  E,  Preuhsler  T,  Bastrup-Birk  A,  2016:  Part  IX:  Meteorological  Measurements.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  14  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Martine Rebetez,  Georg von Arx, Arthur Gessler, Elisabeth Graf Pannatier, John L. Innes, Peter Jakob, Markéta Jetel, Marlen Kube, Magdalena Nötzli, Marcus Schaub, Maria Schmitt, Flurin Sutter, Anne Thimonier, Peter Waldner, Matthias Haeni, 2018: Meteorological data series from Swiss long-term forest ecosystem research plots since 1997. Annals of Forests Science 75: 41: 1-7. [doi: 10.1007/s13595-018-0709-7](https://doi.org/10.1007/s13595-018-0709-7)      *  Haeni, Matthias; von Arx, Georg; Gessler, Arthur; Graf Pannatier, Elisabeth; Innes, John L; Jakob, Peter; Jetel, Markéta; Kube, Marlen; Nötzli, Magdalena; Schaub, Marcus; Schmitt, Maria; Sutter, Flurin; Thimonier, Anne; Waldner, Peter; Rebetez, Martine (2016): Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF) in Switzerland, from 1996-2016. PANGAEA, [doi: 10.1594/PANGAEA.868390](https://doi.org/10.1594/PANGAEA.868390)      *  Gustav Schneiter, Peter Jakob, Martine Rebetez, 2004: Sieben Jahre meteorologische Datenerfassung im Schweizer Wald. Infoblatt Forschungsbereich Wald, Vol 17: 4-6 [>>>](https://www.parcs.ch/snp/pdf_public/2011_schneiteretal_datenerf_wald_wsl_2004.pdf)      *  Jakob P, Sutter F, Waldner P, Schneiter G (2007) Processing remote gauging-data. In: Gomez J. M., Sonnenschein M., Müller M., Welsch H., Rautenstrauch C. (ed.) Information Technologies in Environmental Engineering ITEE 2007, Third International ICSC Symposium, Springer, Berlin, Heidelberg, 211-220.	proprietary
-envidat-lwf-15_2019-03-06	Atmospheric deposition (throughfall and bulk deposition) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815119-ENVIDAT.umm_json	Throughfall (precipitation under forest canopy) is a major pathway in forest nutrient cycling, and its quantification is necessary to establish both water and nutrient budgets. Furthermore, parallel sampling of throughfall and precipitation in the open field (bulk precipitation), together with assumptions about the canopy exchange processes (uptake and leaching of nutrients), allow the atmospheric deposition of nutrients and pollutants to be quantifed. Bulk precipitation and throughfall have been sampled since 1994 or later on 15 LWF plots using 3 (in the open) and 16 (in the forest) funnel-type precipitation collectors. These collectors are replaced by 1 (open area) and 4 (forest stand) snow buckets in winter on plots with abundant precipitation in the form of snow. The length of sampling intervals is usually 14 days.    ### Purpose: ###    To assess a major flux of the water and nutrient budget in forests, and to quantify the atmospheric deposition of nitrogen, sulphur and other nutrients. Atmospheric deposition is one of the key factors in the causal chain between emission of air pollutants and acidifying or eutrophying effects in forest ecosystems.        ### Manual Citation: ###     * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosphärische Deposition: Freiland- und Bestandesniederschläge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.      *  Clarke  N,  Žlindra  D,  Ulrich  E,  Mosello  R,  Derome  J,  Derome  K,  König  N,  Lövblad  G,  Draaijers  GPJ,  Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling,  assessment,  monitoring  and  analysis  of the  effects  of  air  pollution  on  forests.  Thünen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    32    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Schmitt M, Waldner P, Rihm B (2005) Atmospheric deposition on Swiss Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and Assessment, 104: 81-118. [doi: 10.1007/s10661-005-1605-9](http://doi.org/10.1007/s10661-005-1605-9)      *  Thimonier A, Kosonen Z, Braun S, Rihm B, Schleppi P, Schmitt M, Seitler E, Waldner P, Thöni L (2019) Total deposition of nitrogen in Swiss forests: Comparison of assessment methods and evaluation of changes over two decades. Atmospheric Environment, 198: 335-350. [doi: 10.1016/j.atmosenv.2018.10.051](http://doi.org/10.1016/j.atmosenv.2018.10.051)	proprietary
-envidat-lwf-16_2019-03-06	Stemflow LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.02261, 9.06707, 47.47836	https://cmr.earthdata.nasa.gov/search/concepts/C2789815134-ENVIDAT.umm_json	Stemflow (portion of precipitation running down the branches and the trunk and depositing at the base of the tree) can represent a substantial fraction of the total water and nutrient input in stands of smoothbarked species with upright branches. Stemflow was measured with silicone gutters installed on the trunk of 5 trees at three LWF plots during 1-2 years. High capacity containers were used at Novaggio. An automated tipping bucket system, allowing continuous recording of volumes and sampling of representative proportional fraction, is currentlx used at the LWF sites Laegeren, Lausanne, Othmarsingen and Schänis.     ### Purpose: ###    To quantify the contribution of stemflow to the water and nutrient budget and to the atmospheric deposition in selected forests stands.        ### Manual Citation: ###     * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosphärische Deposition: Freiland- und Bestandesniederschläge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.      *  Clarke  N,  Žlindra  D,  Ulrich  E,  Mosello  R,  Derome  J,  Derome  K,  König  N,  Lövblad  G,  Draaijers  GPJ,  Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling,  assessment,  monitoring  and  analysis  of the  effects  of  air  pollution  on  forests.  Thünen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    32    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)	proprietary
-envidat-lwf-17_2019-03-06	Litterfall LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815151-ENVIDAT.umm_json	Litterfall is a key parameter in the biogeochemical cycle of forest ecosystems, linking the tree part to the soil compartment. Litterfall has been collected on 15 LWF plots using 10 traps that are emptied every 4 to 8 weeks since 1996 or later. Both the biomass of the litter and its chemical content (including heavy metals) are measured, in order to quantify the annual return of nutrients and organic matter to the soil. Furthermore, the analysis of the temporal pattern of litterfall production gives insight into possible effects of anthropogenic and natural factors (e.g. severe drought) on the ecosystem and the vitality of the forest stand, provides information on the phenological development of the stand, and, in particular, allows mast years to be identified. At 7 broadleaved sites, litterfall was also used to estimate the leaf area index (LAI) of the forest stand.     ### Purpose: ###    To quantify the annual return of nutrients and organic matter to the soil.         ### Manual Citation: ###     * Thimonier, A., Brang, P., Ottiger, A., 1997. Kapitel C5. Streufall, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-18.      *  Ukonmaanaho  L.,  Pitman  R,  Bastrup-Birk  A,  Breda  N,  Rautio  P,  2016:  Part  XIII:  Sampling  and  Analysis  of  Litterfall.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute for Forests Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [doi: 10.1007/s10342-009-0353-8](http://doi.org/10.1007/s10342-009-0353-8 )	proprietary
-envidat-lwf-18_2019-03-06	Foliar analyses LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815171-ENVIDAT.umm_json	Foliage has been sampled every two years since 1995/1997 on 5-6 trees of the main  species on all LWF plots. Concentrations of macronutrients (N, P, K, Ca, Mg, S), carbon (C) and micronutrients are determined on leaves and current and previous year needles. The dry mass of 100 leaves or 1000 needles is determined as well.    ### Purpose: ###    To assess the nutrient status of the forest stands and detect possible deficiencies or imbalances, which are often indicative of processes at the ecosystem level.        ### Manual Citation: ###     * Brang, P., Hug, C., Thimonier, A., Zehnder, U., 1997. Kapitel D1.5 Nährstoffversorgung von Nadeln und Blättern, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-12.      *  Rautio P, Fürst A, Stefan K, Raitio H, Bartels U, 2016: Part XII: Sampling and Analysis of Needles and Leaves.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on   forests.   Thünen   Institute   of   Forest   Ecosystems,   Eberswalde,   Germany,   19   p.   +   Annex   [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Graf Pannatier E, Schmitt M, Waldner P, Walthert L, Schleppi P, Dobbertin M, Kräuchi N (2010) Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites?. European Journal of Forest Research, 129 (3): 443-461. [doi: 10.1007/s10342-009-0328-9](http://doi.org/10.1007/s10342-009-0328-9)	proprietary
-envidat-lwf-19_2019-03-06	Circular vegetation plots LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815198-ENVIDAT.umm_json	Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relevés were repeatedly carried out at 17 LWF plots in the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). Phytosociological relevés were carried out in one or two concentric circular plots of 30, 200, 400 and 500 m2. All species occurring on the whole area of the LWF plot were also noted during the first vegetation survey.    ### Purpose: ###    To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value.        ### Manual Citation: ###     * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9.      * Canullo  R,  Starlinger  F,  Granke  O,  Fischer  R,  Aamlid  D,  2016:  Part  VI.1:  Assessment  of  Ground  Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  12  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)	proprietary
-envidat-lwf-20_2019-03-06	Permanent vegetation quadrats LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815218-ENVIDAT.umm_json	Ground vegetation is an important compartment of the ecosystem in terms of biodiversity and it takes an active part in the general functioning of the ecosystem. It is also a useful bio-indicator of the site conditions and its monitoring may enable the detection of environmental changes. Ground vegetation relevés were carried out repeatedly at 17 LWF plots during in the the period between 1994 and 2011. In 2013, ground vegetation was surveyed at an additional LWF plot (Laegeren). The cover of all plant species occurring in 16 1-m2 quadrats, distributed over the 43 x 43 m intensive monitoring subplot was visually assessed. Seedlings and saplings were also counted and their position within the quadrat was noted in order to assess tree regeneration.    ### Purpose: ###    To assess the species diversity of ground vegetation and detect possible environmental changes using its bio-indicator value.        ### Manual Citation: ###     * Kull, P., 1997. Kapitel D2. Vegetationsaufnahmen. In: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-9.      * Canullo  R,  Starlinger  F,  Granke  O,  Fischer  R,  Aamlid  D,  2016:  Part  VI.1:  Assessment  of  Ground  Vegetation. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  12  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](http://doi.org/10.1007/s10661-010-1759-y)	proprietary
-envidat-lwf-21_2019-03-06	Leaf area index (LAI) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815247-ENVIDAT.umm_json	Leaf area index (LAI), defined as the total one-sided foliage area per unit ground surface area, is one of the most important characteristics of plant canopy structure. Leaves are the active interface between the atmosphere and the ecosystem. Thus, LAI affects many ecosystem processes, including light and precipitation interception, evapotranspiration, CO2 fluxes and dry deposition. LAI was measured repeatedly in the period 1996-2013 at 18 LWF plots using 1) a LAI-2000 plant canopy analyser (Licor, Inc) and 2) hemispherical photographs of the canopy. Measurements were performed above the 16 vegetation quadrats in the 43 m x 43 m intensive monitoring subplot. In 1996-2003, LAI measurements were usually carried out on the same day as the vegetation surveys. It is also planned to characterise the potential light conditions (diffuse and direct) using the hemispherical photographs of the canopy.    ### Purpose: ###    1) To estimate an important structural parameter of the forest stand, which is needed as an input variable in most ecosystem process models simulating carbon and water cycles on a stand or regional scale; and 2) to document changes in the canopy structure, and thus in light conditions, which may be responsible for changes in ground vegetation        ### Manual Citation: ###     * Thimonier, A., 1997. Kapitel C6. Blattflächenindex (LAI), in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-5.      *  Thimonier, A., 1997. Kapitel C7. Lichtverhältnisse im Wald, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-4.      *  Fleck  S,  Raspe  S,  Cater  M,  Schleppi  P,  Ukonmaanaho  L,  Greve  M,  Hertel  C,  Weis  W,  Rumpf,  S.,  Thimonier, A., Chianucci, F., Beckschäfer, P., 2016: Part XVII: Leaf Area Measurements. In: UNECE ICP Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute    of    Forest    Ecosystems,    Eberswalde,    Germany,    34    p.    +    Annex    [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Thimonier A, Kull P, Keller W, Moser B, Wohlgemuth T (2011) Ground vegetation monitoring in Swiss forests: comparison of survey methods and implications for trend assessments. Environmental Monitoring and Assessment, 174: 47-63. [doi: 10.1007/s10661-010-1759-y](https://doi.org/10.1007/s10661-010-1759-y)      *  Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. European Journal of Forest Research, 129 (4): 543-562. [10.1007/s10342-009-0353-8](https://doi.org/10.1007/s10342-009-0353-8)	proprietary
-envidat-lwf-22_2019-03-06	Passive sampling of NH3 LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29, 46.02, 10.23, 47.48	https://cmr.earthdata.nasa.gov/search/concepts/C2789815262-ENVIDAT.umm_json	NH&#8323; concentrations were measured at 11 LWF plots (1999/2000) with Zürcher passive samplers (Palmes-type diffusion tubes with an acidic solution as absorbent) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NH&#8323; concentrations were measured again at 14 plots, using two Radiello samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.     ### Purpose: ###    To assess air concentrations of ammonia (NH&#8323;) and, using deposition velocities available from the literature, to quantify the dry deposition of NH&#8323; (alternative method to the throughfall method). The LWF plots were part of a larger network covering the main regions of Switzerland. One objective of this larger network was to compare measured and modelled concentrations.	proprietary
-envidat-lwf-23_2019-03-06	Passive sampling of NO2 LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29, 46.02, 10.23, 47.48	https://cmr.earthdata.nasa.gov/search/concepts/C2789815276-ENVIDAT.umm_json	NO&#8322; concentrations were measured at 11 LWF plots (1999/2000) with passive samplers (Palmes-type diffusion tubes) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NO2 concentrations were measured again at 14 plots, using two samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.     ### Purpose: ###    To assess air concentrations of nitrogen dioxide (NO2) and, using deposition velocities available from the literature, to quantify the dry deposition of NO2 (alternative method to the throughfall method).	proprietary
-envidat-lwf-24_2019-03-06	Phenological observations LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.47836	https://cmr.earthdata.nasa.gov/search/concepts/C2789815288-ENVIDAT.umm_json	Phenological observations are recorded every 14 days on LWF plots where throughfall and bulk precipitation are sampled. The percentage of foliage in reference to its maximum potential development in summer, the percentage of foliage with autumnal discoloration and the percentage of fallen leaves (broadleaved stands) are estimated at the plot level. At two LWF plots (Othmarsingen, Vordemwald), phenological stages are documented on individual trees     ### Purpose: ###    To document the seasonal development of the canopy of trees and shrubs at the plot level	proprietary
-envidat-lwf-25_2019-03-06	Soil morphology LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815298-ENVIDAT.umm_json	Description of several morphological soil properties at the beginning of the monitoring campaign. The properties were described for all genetic horizons in soil pits if possible down to the parent material. In heterogeneous LWF-plots, several soil profiles were described in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.    ### Purpose: ###    Morphological soil properties are important for the calculation or interpretation of chemical or physical soil properties or processes. For instance, root distribution is an important input-parameter of water balance models or soil hydromorphy strongly affects the chemical status of soil matrix and soil solution.        ### Manual Citation: ###     * Walthert L, Lüscher P, Luster J, Peter B (2002) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)        ### Paper Citation: ###     * Walthert L, Blaser P, Lüscher P, Luster J, Zimmermann S (2003) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.	proprietary
-envidat-lwf-26_2019-03-06	Soil matrix chemistry LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815029-ENVIDAT.umm_json	Assessment of several chemical soil parameters at the beginning of the monitoring campaign. Most parameters were determined accordung to the manual of ICP-Integrated-Monitoring. The parameters were analysed for all genetic horizons in soil pits and additionally for fixed layers in the Intensive-Monitoring-Plots. In heterogeneous LWF-plots, several soil pits were analysed in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.    ### Purpose: ###    The chemical characterisation of soil matrix down to the paraent material is realised with data from soil pits. The monitoring of the soil matrix in a frequency of roundly 15 years is effected with soil samples from Intensiv-Monitoring-Plots. For soil monitoring, pooled samples with 16 replicats are used down to a depth of 80 cm. The date of the second soil survey is not yet fixed.        ### Manual Citation: ###     * Walthert L, Lüscher P, Luster J, Peter B (2002) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)        ### Paper Citation: ###     * Walthert L, Blaser P, Lüscher P, Luster J, Zimmermann S (2003) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.	proprietary
-envidat-lwf-27_2019-03-06	Matric potential (manual suction cups) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815062-ENVIDAT.umm_json	Measurement of the soil water availability to plants at 10 LWF plots  every 14 days in 5 soil depths (15, 30, 50, 80, 130 cm) with 8 replicates (usually in the IM plot). The range of measurement is from water saturation until-80 kPa.    ### Purpose: ###    The long-term measurement of the soil water availability to plants in the root zone provides useful information about the soil moisture conditions (drought, water saturation, water easily available to plants). The measurement of the soil water suction allows to calibrate the water balance models and to validate the modelled matric potential.        ### Manual Citation: ###     * Peter Waldispühl, 1997: Installation von Tensiometern auf LWF-Flächen. Langfristige Waldökosystem-Forschung LWF, Birmensdorf, 2 S.      *  Peter Waldispühl, Andreas Rigling, 1997: Vorgehen bei der Ablesung von Teniometern auf LWF-Flächen. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 2 S.      *  Peter Waldispühl, 2000: Kurzanleitung für die TensioDB. Langfristige Waldökosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 12 S. + DB-Schema        ### Paper Citation: ###     * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)	proprietary
-envidat-lwf-28_2019-03-06	Soil solution chemistry (lysimeters) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.02261, 9.8888, 47.47836	https://cmr.earthdata.nasa.gov/search/concepts/C2789815111-ENVIDAT.umm_json	Fortnightly measurement of the soil solution chemistry in 4 soil depths  with zero-tension lysimeter below the litter layer and with tension lysimeters at depths of 15, 50 and 80 cm (8 replicates)     ### Purpose: ###    To characterize the chemical status of the soil solution and to detect trends in soil water quality. To assess the effects of air pollution and climate chnage on soil water quality.        ### Manual Citation: ###     * Micha Pluess, Daniel Christen, 1999: Kurzanleitung Bodenlösung. Langfristige Waldökosystem-Forschung LWF, Birmensdorf, 2 S.       * Nieminen  TM,  De  Vos  B,  Cools  N,  König  N,  Fischer  R,  Iost  S,  Meesenburg  H,  Nicolas  M,  O’Dea  P,  Cecchini  G,  Ferretti  M,  De  La  Cruz  A,  Derome  K,  Lindroos  AJ,  Graf  Pannatier  E,  2016:  Part  XI:  Soil  Solution  Collection  and  Analysis.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 20 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)        ### Paper Citation: ###     * Graf Pannatier, E.; Thimonier, A.; Schmitt, M.; Walthert, L.; Waldner, P., 2011: A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?. Environmental Monitoring and Assessment, 174, 1-4: 3-30. [doi: 10.1007/s10661-010-1754-3](http://doi.org/10.1007/s10661-010-1754-3)	proprietary
-envidat-lwf-29_2019-03-06	TDR soil water content measurements LWF Visp	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.85832, 46.29688, 7.85832, 46.29688	https://cmr.earthdata.nasa.gov/search/concepts/C2789815123-ENVIDAT.umm_json	Continuous measurement of soil water content at 15, 50 and 70 cm depth in Visp (3 replications) with TDR soil moisture probes (Tektronix 1502 B) from 2001 until 25.04.2013    ### Purpose: ###    Improve the available data for the calibration or validation of the water balance models, i.e. the determination of the water flux needed for calculating the leaching fluxes.	proprietary
-envidat-lwf-30_2019-03-06	EC-5 soil water content measurement LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.02261, 9.8888, 47.39954	https://cmr.earthdata.nasa.gov/search/concepts/C2789815137-ENVIDAT.umm_json	Continuous measurement of soil water content at 15, 50 and 80 cm depth (3 replications) with ECH2O EC-5 soil moisture probes (Decagon Incorporation, Pullman, WA, USA).      ### Purpose: ###    Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.	proprietary
-envidat-lwf-31_2019-03-06	MPS-2 soil water matric potential LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.85832, 46.29688, 7.85832, 46.29688	https://cmr.earthdata.nasa.gov/search/concepts/C2789815159-ENVIDAT.umm_json	Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors    ### Purpose: ###    Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.	proprietary
-envidat-lwf-32_2019-03-06	MPS-2 on LWF Visp to survey 2017 mortality wave	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.85832, 46.29688, 7.85832, 46.29688	https://cmr.earthdata.nasa.gov/search/concepts/C2789815187-ENVIDAT.umm_json	Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors  1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs    ### Purpose: ###    Explore the effect of shrubs on the water availability for pine trees in Visp.	proprietary
-envidat-lwf-33_2019-03-06	TDR Pfynwald	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.61211, 46.30279, 7.61211, 46.30279	https://cmr.earthdata.nasa.gov/search/concepts/C2789815214-ENVIDAT.umm_json	Continuous measurement of soil water content at one control and    in one irrigated plot in 10, 40 and 60 cm depth (4 replications) with TDR (Tektronix    1502B cable tester, Beaverton, OR, US).    ### Purpose: ###    Monitoring of the soil water content        ### Paper Citation: ###     * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)	proprietary
-envidat-lwf-34_2019-03-06	10-HS Pfynwald	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.61211, 46.30279, 7.61211, 46.30279	https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.umm_json	Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA).    ### Purpose: ###    Monitoring of the soil water matrix potential        ### Paper Citation: ###     * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123)	proprietary
-envidat-lwf-36_2019-03-06	Passive sampling of O3 LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 45.86141, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815256-ENVIDAT.umm_json	Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment: Measurements of mean ozone concentrations with passive samplers (passam ag).        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G,  Lövblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  XV:  Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  11  p.  +  Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](http://doi.org/10.1111/1365-2745.12941)      *  Calatayud V, Diéguez JJ, Sicard P, Schaub M, De Marco A (2016) Testing approaches for calculating stomatal ozone fluxes from passive sampler. Science of the Total Environment. [doi:10.1016/j.scitotenv.2016.07.155](http://doi.org/10.1016/j.scitotenv.2016.07.155)      *  Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](http://doi.org/10.1016/B978-0-08-098222-9.00019-4)	proprietary
-envidat-lwf-37_2019-03-06	Continuous measurement of O3 LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 45.86141, 9.06707, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815274-ENVIDAT.umm_json	Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment: Continuous measurements of ozone concentrations        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G,  Lövblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  XV:  Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  11  p.  +  Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](https://doi.org/10.1111/1365-2745.12941)      *  Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](https://doi.org/10.1016/B978-0-08-098222-9.00019-4)	proprietary
-envidat-lwf-38_2019-03-06	Symptoms of O3 injuries LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815286-ENVIDAT.umm_json	Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling.     ### Purpose: ###    Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms        ### Manual Citation: ###     * Schaub  M,  Calatayud  V,  Ferretti  M,  Brunialti  G, Lövblad  G,  Krause  G,  Sanz  MJ,  2016:  Part  VIII:  Monitoring  of  Ozone  Injury.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf)      *  Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X)	proprietary
-envidat-lwf-45_2019-03-06	Tree Diameter and Height Inventory LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815297-ENVIDAT.umm_json	Tree circumference, height, height to crown base measurements, mortality, decay class and removal assessment on LWF plots    ### Purpose: ###    Assessment of tree and forest growth        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, 1999: Vorläufige Feld-Aufnahmeanleitung für die BHU- und Höhen-Inventur auf LWF-Flächen (V1.0), Langfristige Waldökosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 18 S.      *  Christian Hug, Matthias Dobbertin, Chris Nussbaumer, Yves Stettler, 2010: Provisorische Aufnahmeanleitung für die Brusthöhenumfang- und Höheninventur auf LWF-Flächen. Langfristige Waldökosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 29 S.      *  Christian Hug, Chris Nussbaumer, Yves Stettler, 2014: Aufnahmeanleitung für die Brusthöhenumfang und Höheninventur auf LWF-Flächen. Langfristige Waldökosystem-Forschung, Eidg. Forschungsanstalt WSL, Birmensdorf, 36 S.      *  Dobbertin  M,  Neumann  M,  2016:  Part  V:  Tree  Growth.  In:  UNECE  ICP  Forests,  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://dx.doi.org/10.1016/j.foreco.2013.05.040)	proprietary
-envidat-lwf-47_2019-03-06	Crown Condition Assessment and Damage Cause Assessment Sanasilva	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815309-ENVIDAT.umm_json	Annual Crown Condition Assessment including mortality and removal and Damage Causse Assessment on the Sanasilva-Sites and LWF plots.    ### Purpose: ###    To assess tree and forest health and its changes and to assess occurence and extent of diseases        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Flächen (Version 10). Sanasilva Inventur und Langfristige Waldökosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)      *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schröck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )        ### Paper Citation: ###     * BAFU (2017) Jahrbuch Wald und Holz 2017. Umwelt-Zustand, Bundesamt für Umwelt, Bern, Vol. 1718: 110 p. [>>>](http://www.bafu.admin.ch/uz-1718-d)     *  Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [Technical Reports](http://icp-forests.net/page/icp-forests-technical-report)      *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gefährdung des Schweizer Waldes – eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)	proprietary
-envidat-lwf-48_2019-03-06	Crown Condition Assessment and Damage Cause Assessment LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815341-ENVIDAT.umm_json	Assessment of damges, i.e. symptoms, extent and causes on trees.    ### Purpose: ###    Occurrence and extent of diseases        ### Manual Citation: ###     * Matthias Dobbertin, Christian Hug, Andreas Schwyzer, Serge Borer, Hanna Schmalz, 2016: Aufnahmeanleitung Kronenansprachen auf den Sanasilva- und den LWF-Flächen (Version 10). Sanasilva Inventur und Langfristige Waldökosystem-Forschung, Birmensdorf, 86 S. [>>>](https://www.wsl.ch/fileadmin/user_upload/WSL/Wald/Waldentwicklung_Monitoring/LWF/Sanasilva/ssi_anleitung_v10_extern.pdf)      *  Eichhorn  J,  Roskams  P,  Potocic  N,  Timmermann  V,  Ferretti  M,  Mues  V,  Szepesi  A,  Durrant  D,  Seletkovic   I,   Schröck   H-W,   Nevalainen   S,   Bussotti   F,   Garcia   P,   Wulff   S,   2016:   Part   IV:   Visual   Assessment  of  Crown  Condition  and  Damaging Agents.  In:  UNECE  ICP  Forests  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 49 p. + Annex [>>>](https://www.icp-forests.org/pdf/manual/2016/ICP_Manual_2017_02_part04.pdf )        ### Paper Citation: ###     * Michel A, Seidling W, Prescher A K (2018) Forest Condition in Europe. 2018 Technical Report of ICP Forests. Report under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). BFW-Dokumentation 25/2018, BFW Austrian Research Centre for Forests, Vienna, 92 p. [>>>](http://icp-forests.net/page/icp-forests-technical-report)      *  Köhl M, San-Miguel-Ayanz J, Cools N, de Vos B, Fischer R, Camia A, Granke O, Hiederer R, Lorenz M, Montanarella L, Mues V, Nagel H-D, Poker J, Scheuschner T, Schlutow A (2011) Maintenance of Forest Ecosystem Health and Vitality. State of Europe s forests: status and trends in sustainable forest management in Europe 29-49. [>>>](http://www.foresteurope.org/documentos/State_of_Europes_Forests_2011_Report_Revised_November_2011.pdf)     *  Brang P., 1998: Sanasilva-Bericht 1997. Zustand und Gefährdung des Schweizer Waldes – eine Zwischenbilanz nach 15 Jahren Waldschadenforschung. Berichte der Eidg. Forschungsanstalt für Wald, Schnee und Landschaft, Vol. 345. Eidg. Forschungsanstalt WSL, Birmensdorf, 102 S. [>>>](https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14555)	proprietary
-envidat-lwf-49_2019-03-06	Deadwood survey LWF 1995 - line intersect	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815039-ENVIDAT.umm_json	Assessment of coarse woody debris on LWF plots using the line intersect method     ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.        ### Paper Citation: ###     * Bretz Guby, N.A., Dobbertin, M., 1996. Quantitative estimates of coarse woody debris and standing dead trees in selected Swiss forests. Glob. Ecol. Biogeogr. Lett. 5, 327-341.	proprietary
-envidat-lwf-50_2019-03-06	Deadwood survey LWF 2005 - subplot (Forets Biota)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815071-ENVIDAT.umm_json	Assessment of coarse woody debris on LWF plots using full count methods on defined subplots (applying ForestBiota protocoll)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Franziska Heinrich, 2005: Totholz-Aufnahme mit ForestBIOTA Protokoll. Langfristige Waldökosystem-Forschung LWF. Eidg. Forschungsanstalt WSL, Birmensdorf, 7 S.      *  ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)      *  Fischer, R., Fischer, R., Seidling, W., Granke, O., Meyer, P., Stofer, S., Travaglini, D., 2007. ForestBIOTA – Testphase zur Erfassung der biologischen Vielfalt. AFZ/Wald 62, 1070.        ### Paper Citation: ###     * Seidling, W., Travaglini, D., Meyer, P., Waldner, P., Fischer, R., Granke, O., Chirici, G., Corona, P., 2014. Dead wood and stand structure - relationships for forest plots across Europe. IForest - Biogeosciences and Forestry 7, 269-281.	proprietary
-envidat-lwf-51_2019-03-06	Deadwood survey LWF 2013 - subplot	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815114-ENVIDAT.umm_json	Assessment of coarse woody debris on LWF plots using the line intersect method and full count methods on subplots (repetition of the 2005 survey)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * ForestBiota, 2005. Stand structure assessment including deadwood. EU/ICP Forests Biodiversity Test-Phase (ForestBIOTA). [>>>](http://www.forestbiota.org)	proprietary
-envidat-lwf-52_2019-03-06	Deadwood survey LWF 2013 - line intersect	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815126-ENVIDAT.umm_json	Assessment of coarse woody debris on LWF plots using the line intersect method (repetition of the 1995 survey)    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.	proprietary
-envidat-lwf-53_2019-03-06	Deadwood survey Sanasilva 2013	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815144-ENVIDAT.umm_json	Assessment of coarse woody debris on Sanasilva plots (16x16 km grid) using the full count methods on subplots applying BioSoil protocoll    ### Purpose: ###    Assessing the amount of coarse woody debris        ### Manual Citation: ###     * A. Bastrup-Birk, P. Nevile, G. Chirici, T. Houston, 2006: The BioSoil ForestBiodiversity Field Manual Version 1.0/1.1/1.1A for the Field Assessment 2006-07. Working Group on ForestBiodiversity, Forest Focus Demonstration Project BioSoil 2004-2005, 47 S.	proprietary
-envidat-lwf-54_2019-03-06	Sapflow measurements LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.41665, 46.02261, 9.85521, 47.22516	https://cmr.earthdata.nasa.gov/search/concepts/C2789815161-ENVIDAT.umm_json	Continuous sap flow measurements with Granier-needles to investigate carbon balance and water relations of trees    ### Purpose: ###    Assessment of water cycle processes	proprietary
-envidat-lwf-56_2019-03-06	Manual circumference band measurement LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815183-ENVIDAT.umm_json	Tree circumference change measurements from plastic girth bands    ### Purpose: ###    Assessment of annual tree stem growth         ### Manual Citation: ###     * Dobbertin  M,  Neumann  M,  2016:  Part  V:  Tree  Growth.  In:  UNECE  ICP  Forests,  Programme  Co-ordinating  Centre  (ed.):  Manual  on  methods  and  criteria  for  harmonized  sampling,  assessment,  monitoring  and  analysis  of  the  effects  of  air  pollution  on  forests.  Thünen  Institute  of  Forest  Ecosystems, Eberswalde, Germany, 17 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)         ### Paper Citation: ###     * Etzold S, Waldner P, Thimonier A, Schmitt M, Dobbertin M (2014) Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter. Forest Ecology and Management, 311: 41-55. [doi: 10.1016/j.foreco.2013.05.040](http://doi.org/10.1016/j.foreco.2013.05.040)	proprietary
-envidat-lwf-57_2019-03-06	Automated point dendrometer measurements at LWF sites	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.83416, 46.02261, 9.85521, 46.81535	https://cmr.earthdata.nasa.gov/search/concepts/C2789815203-ENVIDAT.umm_json	Continuous stem radius measurements to investigate carbon balance and water relations of trees    ### Purpose: ###    Assessment of growth and water related stem changes	proprietary
-envidat-lwf-81_2019-03-06	Dendrochronological analyses of tree core samples (dominant trees) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815223-ENVIDAT.umm_json	Two stem core samples of 12 to 20 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 12 pre-dominant or dominant trees and if possible also 4 subdominant or surpressed trees. NOTE: The samplings were carried out between 1996 and 1999 for most plots and in 2003 for the plot Lantsch. The cores cover a time span depending on the age of the oldest trees on a plot. On one plot the oldest sampled tree ring grew in the year 1646.     ### Purpose: ###    Reconstruction of stand history and tree growth        ### Manual Citation: ###     * Paolo Cherubini, Matthias Dobbertin, 1997: Bestandesgeschichte (Dendrochronologie). In: Peter Brang (ed.), Aufnahmeanleitung LWF. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 3 S.      *  Cherubini, P.; Dobbertin, M., 1998: The Swiss long-term forest ecosystem research: methods for reconstructing forest history. In: Borghetti, M. (ed): Società Italiana di Selvicoltura ed Ecologia Forestale (SISEF), Atti I: 19-22.        ### Paper Citation: ###     * Cherubini, P., Fontana, G., Rigling, D., Dobbertin, M., Brang, P., Innes, J.L., 2002. Tree-life history prior to death: two fungal root pathogens affect tree-ring growth differently. J. Ecol. 90, 839-850.	proprietary
-envidat-lwf-82_2019-03-06	Dendrochronological analyses of tree core samples (CATS) adjacent to LWF sites	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.58377, 8.53568, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815251-ENVIDAT.umm_json	There were 2 cores taken at 1.3 m height from each of 10 trees outside the LWF plot    ### Purpose: ###    influence of drought and nutrient availabiliy on tree growth        ### Paper Citation: ###     * Lévesque, M., Walthert, L., Weber, P., 2016. Soil nutrients influence growth response of temperate tree species to drought. J. Ecol. 104, 377-387.	proprietary
-envidat-lwf-83_2019-03-06	Dendrochronological analyses of tree core samples (IsoN) adjacent to LWF sites	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.58377, 8.71258, 47.39954	https://cmr.earthdata.nasa.gov/search/concepts/C2789815271-ENVIDAT.umm_json	Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees.     ### Purpose: ###    N and C stable isotope signals        ### Paper Citation: ###     * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.	proprietary
-envidat-lwf-84_2019-03-06	Dendrochronological analyses of tree core samples (subplot) LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.88676, 46.81535, 9.85521, 47.47836	https://cmr.earthdata.nasa.gov/search/concepts/C2789815285-ENVIDAT.umm_json	Two stem core samples of all trees of a subplot of approximately 1 a. Basal Area, Wood volume increment per area estimation    ### Purpose: ###    Investigation of the relation between dendroparameters of dominance/surpression and stem growth. Abgleich Jahrringdaten mit Inventurdaten.        ### Paper Citation: ###     * Nehrbass-Ahles C, Babst F, Klesse S, Nötzli M, Bouriaud O, Neukom R, Dobbertin M, Frank D (2014) The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology, 20 (9): 2867–2885. [doi: 10.1111/gcb.12599](http://doi.org/10.1111/gcb.12599)     *  Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311.	proprietary
-envidat-lwf-86_2019-03-06	Deadwood sampling at Sanasilva and LWF sites	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29085, 46.02261, 10.23009, 47.6837	https://cmr.earthdata.nasa.gov/search/concepts/C2789815296-ENVIDAT.umm_json	Sampling of deadwood for density and chemical analysis during summer 2009    ### Purpose: ###    Determination of N and C pools of deadwood        ### Paper Citation: ###     * WEGGLER, K.; DOBBERTIN, M.; JÜNGLING, E.; KAUFMANN, E.; THÜRIG, E., 2012. Dead wood volume to dead wood carbon: the issue of conversion factors. European Journal of Forest Research 131, 1423-1438.	proprietary
-envidat-lwf-87_2019-03-06	Stem discs LWF	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.43594, 46.26856, 9.85521, 47.39954	https://cmr.earthdata.nasa.gov/search/concepts/C2789815308-ENVIDAT.umm_json	Measurement of tree ring widts in tree stem disks according to 'Bräker O.U. (1993) Anleitung zur Entnahme von Stammscheiben auf Ertragskundeflächen'    ### Purpose: ###    tree growth	proprietary
-envidat_232_1.0	Reproducibility Dataset for CRYOWRF v1.0	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815061-ENVIDAT.umm_json	"This repository contains data required for reproducibility of the results to be published in the associated manuscript.     Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.     The datasets consist of two folders organized in zip format:     1. REPRODUCIBILITY_SIMULATION:        Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript      Additional files include datasets (from IMAU-FDM / RACMO, see ""credits"" below ) as well as helper python scripts to       produce *.sno files       which are used as initial conditions for SNOWPACK in CRYOWRF.     2. REPRODUCIBILITY_POSTPROCESSING:        Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.     Each of the folders have their own readme files for more details.     ### Code  citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165    location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF     (stable releases / institutional repo) https://github.com/vsharma-next/CRYOWRF       (dev branches  / developer repo)      ### Publication  **Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**  Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development    ### Acknowledgements  We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:    * Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015     * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016).  The Cryosphere, 12, doi:10.5194/tc-12-1643-2018"	proprietary
-environmental-constraints-on-tree-growth_1.0	Environmental constraints on tree growth	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815329-ENVIDAT.umm_json	Seasonal variation in environmental constraints (vapor pressure deficit – VPD, air temperature, and soil moisture) on tree growth for the potential distribution range of seven widespread Central European tree species. We simulated environmental constraints on growth fusing 3-PG model or the species’ potential distribution range within the forested area of Switzerland on a 1×1 km grid for seven dominant tree species: _Larix decidua_, _Picea abies_, _Abies alba_, _Fagus sylvatica_, _Acer pseudoplatanus_, _Pinus sylvestris_, and _Quercus robur_. For this purpose, we simulated the growth of these tree species in monocultures with the average climate observed during 1961–1990 or 1991-2018. The stands were initialized as 2-year-old plantations with an initial density of 2,500 trees ha-1 and simulated until the age of 30 years. For each simulated month, we obtained the relative contribution of environmental constraints (VPD, temperature, and soil water) on tree growth.	proprietary
 environmental_layers_1	Marine environmental data layers for Southern Ocean species distribution modelling	AU_AADC STAC Catalog	1955-01-01	2017-12-31	-180, -80, 180, -45	https://cmr.earthdata.nasa.gov/search/concepts/C1546333934-AU_AADC.umm_json	This dataset is a collection of marine environmental data layers suitable for use in Southern Ocean species distribution modelling. All environmental layers have been generated at a spatial resolution of 0.1 degrees, covering the Southern Ocean extent (80 degrees S - 45 degrees S, -180 - 180 degrees). The layers include information relating to bathymetry, sea ice, ocean currents, primary production, particulate organic carbon, and other oceanographic data.  An example of reading and using these data layers in R can be found at  https://australianantarcticdivision.github.io/blueant/articles/SO_SDM_data.html.  The following layers are provided:  1. Layer name: depth Description: Bathymetry. Downloaded from GEBCO 2014 (0.0083 degrees = 30sec arcmin resolution) and set at resolution 0.1 degrees. Then completed with the bathymetry layer manually corrected and provided in Fabri-Ruiz et al. (2017) Value range: -8038.722 - 0 Units: m  Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Fabri-Ruiz S, Saucede T, Danis B and David B (2017). Southern Ocean Echinoids database_An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, (697), 1.  2. Layer name: geomorphology Description: Last update on biodiversity.aq portal. Derived from  O'Brien et al. (2009) seafloor geomorphic feature dataset. Mapping based on GEBCO contours, ETOPO2, seismic lines). 27 categories Value range: 27 categories Units: categorical Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10  3. Layer name: sediments Description: Sediment features Value range: 14 categories Units: categorical Source: Griffiths 2014 (unpublished) URL: http://share.biodiversity.aq/GIS/antarctic/  4. Layer name: slope Description: Seafloor slope derived from bathymetry with the terrain function of raster R package. Computation according to Horn (1981), ie option neighbor=8. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Unit set at degrees. Value range: 0.000252378 - 16.94809 Units: degrees Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69:14-47  5. Layer name: roughness Description: Seafloor roughness derived from bathymetry with the terrain function of raster R package. Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Value range: 0 - 5171.278 Units: unitless Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/  6. Layer name: mixed layer depth Description: Summer mixed layer depth climatology from ARGOS data. Regridded from 2-degree grid using nearest neighbour interpolation Value range: 13.79615 - 461.5424 Units: m Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data  7. Layer name: seasurface_current_speed Description: Current speed near the surface (2.5m depth), derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS model) Value range: 1.50E-04 - 1.7 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data  8. Layer name: seafloor_current_speed Description: Current speed near the sea floor, derived from the CAISOM model  (Galton-Fenzi et al. 2012, based on ROMS) Value range: 3.40E-04 - 0.53 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data  9. Layer name: distance_antarctica Description: Distance to the nearest part of the Antarctic continent Value range: 0 - 3445 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data  10. Layer name: distance_canyon Description: Distance to the axis of the nearest canyon Value range: 0 - 3117 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data  11. Layer name: distance_max_ice_edge Description: Distance to the mean maximum winter sea ice extent (derived from daily estimates of sea ice concentration) Value range: -2614.008 - 2314.433 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data  12. Layer name: distance_shelf Description: Distance to nearest area of seafloor of depth 500m or shallower Value range: -1296 - 1750 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data  13. Layer name: ice_cover_max Description: Ice concentration fraction, maximum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  14. Layer name: ice_cover_mean Description: Ice concentration fraction, mean on [1957-2017] time period Value range: 0 - 0.9708595 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  15. Layer name: ice_cover_min Description: Ice concentration fraction, minimum on [1957-2017] time period Value range: 0 - 0.8536261 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  16. Layer name: ice_cover_range Description: Ice concentration fraction, difference maximum-minimum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  17. Layer name: ice_thickness_max Description: Ice thickness, maximum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  18. Layer name: ice_thickness_mean Description: Ice thickness, mean on [1957-2017] time period Value range: 0 - 1.614133 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  19. Layer name: ice_thickness_min Description: Ice thickness, minimum on [1957-2017] time period Value range: 0 - 0.7602701 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  20. Layer name: ice_thickness_range Description: Ice thickness, difference maximum-minimum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis  21. Layer name: chla_ampli_alltime_2005_2012 Description: Chlorophyll-a concentrations obtained from MODIS satellite data. Amplitude of pixel values (difference between maximal and minimal value encountered by each pixel during all months of the period [2005-2012]) Value range: 0 - 77.15122 Units: mg/m^3 Source: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/ URL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php  22. Layer name: chla_max_alltime_2005_2012 Description: Chlorophyll-a concentrations obtained from MODIS satellite data. Maximal value encountered by each pixel during all months of the period [2005-2012] Value range: 0 - 77.28562 Units: mg/m^3 Source: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/ URL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php  23. Layer name: chla_mean_alltime_2005_2012 Description: Chlorophyll-a concentrations obtained from MODIS satellite data. Mean value of each pixel during all months of the period [2005-2012] Value range: 0 - 30.42691 Units: mg/m^3 Source: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/ URL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php  24. Layer name: chla_min_alltime_2005_2012 Description: Chlorophyll-a concentrations obtained from MODIS satellite data. Minimal value encountered by each pixel during all months of the period [2005-2012] Value range: 0 - 29.02929 Units: mg/m^3 Source: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/ URL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php  25. Layer name: chla_sd_alltime_2005_2012 Description: Chlorophyll-a concentrations obtained from MODIS satellite data. Standard deviation value of each pixel during all months of the period [2005-2012] Value range: 0 - 27.9877 Units: mg/m^3 Source: https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/Monthly/9km/chlor_a/ URL: https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php  26. Layer name: POC_2005_2012_ampli Description: Particulate organic carbon, model Lutz et al. (2007). Amplitude value (difference maximal and minimal value, see previous layers) all seasonal layers [2005-2012] Value range: 0 - 1.31761 Units: g/m^2/d Source: This study. Following Lutz et al. (2007) URL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers Citation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).  27. Layer name: POC_2005_2012_max Description: Particulate organic carbon, model Lutz et al. (2007). Maximal value encountered on each pixel among all seasonal layers [2005-2012] Value range: 0.00332562 - 1.376601 Units: g/m^2/d Source: This study. Following Lutz et al. (2007) URL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers Citation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).  28. Layer name: POC_2005_2012_mean Description: Particulate organic carbon, model Lutz et al. (2007). Mean all seasonal layers [2005-2012] Value range: 0.003184335 - 0.5031364 Units: g/m^2/d Source: This study. Following Lutz et al. (2007) URL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers Citation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).  29. Layer name: POC_2005_2012_min Description: Particulate organic carbon, model Lutz et al. (2007). Minimal value encountered on each pixel among all seasonal layers [2005-2012] Value range: 0.003116508 - 0.1313119 Units: g/m^2/d Source: This study. Following Lutz et al. (2007) URL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers Citation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).  30. Layer name: POC_2005_2012_sd Description: Particulate organic carbon, model Lutz et al. (2007). Standard deviation all seasonal layers [2005-2012] Value range: 3.85E-08 - 0.4417001 Units: g/m^2/d Source: This study. Following Lutz et al. (2007) URL: https://data.aad.gov.au/metadata/records/Particulate_carbon_export_flux_layers Citation: Lutz MJ, Caldeira K, Dunbar RB and Behrenfeld MJ (2007). Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. Journal of Geophysical Research: Oceans, 112(C10).  31. Layer name: seafloor_oxy_1955_2012_ampli Description: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor oxygen concentration over [1955-2012], modified from WOCE Value range: 0.001755714 - 5.285187 Units: mL/L Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  32. Layer name: seafloor_oxy_1955_2012_max Description: Maximum value encountered for each pixel on all month layers of oxygen concentration over [1955-2012], modified from WOCE Value range: 3.059685 - 11.52433 Units: mL/L Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  33. Layer name: seafloor_oxy_1955_2012_mean Description: Mean seafloor oxygen concentration over [1955-2012] (average of all monthly layers), modified from WOCE Value range: 2.836582 - 8.858084 Units: mL/L Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  34. Layer name: seafloor_oxy_1955_2012_min Description: Minimum value encountered for each pixel on all month layers of seafloor oxygen concentration over [1955-2012], modified from WOCE Value range: 0.4315577 - 8.350794 Units: mL/L Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  35. Layer name: seafloor_oxy_1955_2012_sd Description: Standard deviation seafloor oxygen concentration over [1955-2012]  (of all monthly layers), modified from WOCE Value range: 0.000427063 - 1.588707 Units: mL/L Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  36. Layer name: seafloor_sali_2005_2012_ampli Description: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE Value range: 0.000801086 - 4.249901 Units: PSU Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  37. Layer name: seafloor_sali_2005_2012_max Description: Maximum value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE Value range: 32.90105 - 35.3997 Units: PSU Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  38. Layer name: seafloor_sali_2005_2012_mean Description: Mean seafloor salinity over [2005-2012] (average of all monthly layers), modified from WOCE Value range: 32.51107 - 35.03207 Units: PSU Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  39. Layer name: seafloor_sali_2005_2012_min Description: Minimum value encountered for each pixel on all month layers of seafloor salinity over [2005-2012], modified from WOCE Value range: 29.8904 - 34.97735 Units: PSU Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  40. Layer name: seafloor_sali_2005_2012_sd Description: Standard deviation seafloor salinity over [2005-2012]  (of all monthly layers), modified from WOCE Value range: 0.000251834 - 1.36245 Units: PSU Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  41. Layer name: seafloor_temp_2005_2012_ampli Description: Amplitude (difference maximum-minimum) value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE Value range: 0.0086 - 8.625669 Units: degrees C Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  42. Layer name: seafloor_temp_2005_2012_max Description: Maximum value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE Value range: -2.021455 - 15.93171 Units: degrees C Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  43. Layer name: seafloor_temp_2005_2012_mean Description: Mean seafloor temperature over [2005-2012] (average of all monthly layers), modified from WOCE Value range: -2.085796 - 13.23161 Units: degrees C Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  44. Layer name: seafloor_temp_2005_2012_min Description: Minimum value encountered for each pixel on all month layers of seafloor temperature over [2005-2012], modified from WOCE Value range: -2.1 - 11.6431 Units: degrees C Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  45. Layer name: seafloor_temp_2005_2012_sd Description: Standard deviation seafloor temperature over [2005-2012]  (of all monthly layers), modified from WOCE Value range: 0.002843571 - 2.877084 Units: degrees C Source: Derived from World Ocean Circulation Experiment 2013 URL: https://www.nodc.noaa.gov/OC5/woa13/woa13data.html  46. Layer name: extreme_event_max_chl_2005_2012_ampli Description: Amplitude (difference maximum-minimum) number of the number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 3 Units: unitless Source: derived from chlorophyll-a concentration layers  47. Layer name: extreme_event_max_chl_2005_2012_max Description: Maximum number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 5 Units: unitless Source: derived from chlorophyll-a concentration layers  48. Layer name: extreme_event_max_chl_2005_2012_mean Description: Mean of the number of extreme events calculated between 2005 and 2012 Value range: 0 - 3.875 Units: unitless Source: derived from chlorophyll-a concentration layers  49. Layer name: extreme_event_max_chl_2005_2012_min Description: Minimum number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 5 Units: unitless Source: derived from chlorophyll-a concentration layers  50. Layer name: extreme_event_min_chl_2005_2012_ampli Description: Amplitude (difference maximum-minimum) number of the number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 9 Units: unitless Source: derived from chlorophyll-a concentration layers  51. Layer name: extreme_event_min_chl_2005_2012_max Description: Maximum number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 11 Units: unitless Source: derived from chlorophyll-a concentration layers  52. Layer name: extreme_event_min_chl_2005_2012_mean Description: Mean of the number of extreme events calculated between 2005 and 2012 Value range: 0 - 11 Units: unitless Source: derived from chlorophyll-a concentration layers  53. Layer name: extreme_event_min_chl_2005_2012_min Description: Minimum number of extreme events calculated between 2005 and 2012 Value range: integer values 0 - 11 Units: unitless Source: derived from chlorophyll-a concentration layers  54. Layer name: extreme_event_min_oxy_1955_2012_nb Description: Number of extreme events (minimal seafloor oxygen concentration records) that happened between January and December of the year Value range: integer values 0 - 12 Units: per year Source: derived from seafloor oxygen concentration layers  55. Layer name: extreme_event_max_sali_2005_2012_nb Description: Number of extreme events (maximal seafloor salinity records) that happened between January and December of the year Value range: integer values 0 - 12 Units: per year Source: derived from seafloor salinity layers  56. Layer name: extreme_event_min_sali_2005_2012_nb Description: Number of extreme events (minimal seafloor salinity records) that happened between January and December of the year Value range: integer values 0 - 12 Units: per year Source: derived from seafloor salinity layers  57. Layer name: extreme_event_max_temp_2005_2012_nb Description: Number of extreme events (maximal seafloor temperature records) that happened between January and December of the year Value range: integer values 0 - 12 Units: per year Source: derived from seafloor temperature layers  58. Layer name: extreme_event_min_temp_2005_2012_nb Description: Number of extreme events (minimal seafloor temperature records) that happened between January and December of the year Value range: integer values 0 - 12 Units: per year Source: derived from seafloor temperature layers	proprietary
 er2_aerial_photos_722_1	SAFARI 2000 ER-2 Color-IR Aerial Photography, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-13	2000-09-25	15, -30, 43, -12	https://cmr.earthdata.nasa.gov/search/concepts/C2788402747-ORNL_CLOUD.umm_json	Aerial photography from the NASA ER-2 high-altitude aircraft was collected to provide detailed and spatially extensive documentation over parts of the SAFARI study area. The ER-2 aerial photography consists of 3,046 color-infrared (IR) transparencies collected during the SAFARI 2000 Dry Season Aircraft Campaign in August and September of 2000. ORNL DAAC has archived scanned subsets of the ER-2 aerial photography. In addition, 515 image frames have been scanned from copies of the original level-0 ER-2 aerial photography by the University of the Witwatersrand (Wits), in Pretoria, South Africa. ORNL DAAC has archived subsets of the available imagery from ARC and Wits.	proprietary
 er2edop_1	CAMEX-3 ER-2 Doppler Radar (EDOP)	GHRC_DAAC STAC Catalog	1998-08-08	1998-09-27	-89.971, 13.976, -63.22, 34.588	https://cmr.earthdata.nasa.gov/search/concepts/C1995565983-GHRC_DAAC.umm_json	The CAMEX-3 ER-2 Doppler Radar (EDOP) dataset is a browse-only dataset that consists of plotted reflectivity and Doppler velocity data collected by the ER-2 Doppler Radar (EDOP) during the third field campaign in the Convection And Moisture EXperiment (CAMEX) series, CAMEX-3. This field campaign took place from August to September 1998 based out of Patrick Air Force Base in Florida, with the purpose of studying the various aspects of tropical cyclones in the region. EDOP was mounted onboard the NASA ER-2 high-altitude research aircraft from which it obtained vertical profiles of convection within tropical cyclones. The daily browse files are available from August 5 through September 27, 1998 in GIF format.	proprietary
@@ -16072,16 +15692,8 @@ er2nasti_1	CAMEX-3 NAST-I RADIANCE PRODUCTS V1	GHRC_DAAC STAC Catalog	1998-08-13
 er2nav_1	CAMEX-3 ER-2 NAVIGATION V1	GHRC_DAAC STAC Catalog	1998-08-08	1998-09-27	-89.971, 13.976, -63.22, 34.588	https://cmr.earthdata.nasa.gov/search/concepts/C1979112721-GHRC_DAAC.umm_json	The CAMEX-3 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) it contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data are available in ASCII text file format and Graphics Interchange Format, where each file contains data recorded at one second intervals for each flight.	proprietary
 er2navimpacts_1	ER-2 Navigation Data IMPACTS	GHRC_DAAC STAC Catalog	2020-01-15	2023-03-02	-118.284, 26.907, -64.894, 48.658	https://cmr.earthdata.nasa.gov/search/concepts/C1995566252-GHRC_DAAC.umm_json	The NASA ER-2 Navigation Data IMPACTS dataset contains information recorded by the onboard navigation and data collection systems of the NASA ER-2 high-altitude research aircraft. In addition to typical navigation data (e.g., date, time, latitude/longitude, and altitude) it also contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data were collected during the  Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands;  and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The IMPACTS navigation dataset files are available from January 15, 2020, through March 2, 2023, in ASCII-ict format.	proprietary
 erbe_albedo_monthly_xdeg_957_1	ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990	ORNL_CLOUD STAC Catalog	1986-01-01	1990-02-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784882228-ORNL_CLOUD.umm_json	This data set, ISLSCP II Earth Radiation Budget Experiment (ERBE) Monthly Albedo, 1986-1990, contains both the original ERBE albedo data at 2.5 degree spatial resolution, and the International Land Surface Climatology Project Initative II (ISLSCP Initiative II) albedo product re-gridded to 1 degree resolution. The goals of the ERBE were (1) to understand the radiation balance between the Sun, Earth, atmosphere, and space and (2) to establish an accurate, long-term baseline data set for detection of climate changes. Earth Radiation Budget (ERB) data are fundamental to the development of realistic climate models and to the understanding of natural and anthropogenic perturbations of the climate system. As part of ERBE, measurements of broadband shortwave radiation reflected from the Earth-atmosphere system were obtained, from which top of atmosphere albedo values were calculated. In addition, values from scenes determined to be free of clouds were analyzed separately and clear-sky albedos were derived. For this study, only the clear-sky albedos are included. The ERBE data sets for ISLSCP Initiative II contain global, top of atmosphere, clear sky albedo data from January 1986 to February 1990.	proprietary
-escarpment-evolution-drives-the-diversification-of-the-madagascar-flora_1.0	Escarpment evolution drives the diversification of the Madagascar flora	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	42.3632812, -26.6684045, 51.7675781, -11.3522326	https://cmr.earthdata.nasa.gov/search/concepts/C3226082034-ENVIDAT.umm_json	Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.	proprietary
-espon-digiplan_1.0	ESPON Digiplan	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	6.5478516, 46.0244304, 14.3701172, 54.6331536	https://cmr.earthdata.nasa.gov/search/concepts/C2789815052-ENVIDAT.umm_json	The dataset as a part of the international project ESPON Digiplan. The aim of this international project is to assess the extent, organisation and financing of digitisation of plan data as well as the use of these data in ESPON member countries. As a part of the in-depth case study, 7 virtual expert interviews in Switzerland and 5 virtual expert interviews in Germany were conducted with experts on the topic of digitisation of plan data. The documents contain the transcripts of the interviews. The transcripts aim to capture the content of the interviews, which is why voice raising and lowering, as well as pauses in the interview, were not specifically recorded. The interviews were conducted in German, therefore the transcripts are also in German.	proprietary
 eta_model_723_1	SAFARI 2000 ETA Atmospheric Model Data, Wet and Dry Seasons 2000	ORNL_CLOUD STAC Catalog	2000-02-01	2000-09-30	-13, -53, 53, -9	https://cmr.earthdata.nasa.gov/search/concepts/C2788405580-ORNL_CLOUD.umm_json	With modern computer power now capable of making mesoscale model output available in real time in the operational environment, increased attention has been given to utilizing these models in order to improve the forecasting ability of meteorologists. The National Centers for Environmental Prediction (NCEP) has developed a step-mountain eta coordinate model generally known as the ETA Model.This NCEP ETA data assimilation and prediction system (see Mesinger et al., 1988; Black, 1994) has been used by the South African Weather Bureau/Service (SAWS) to provide operational regional forecast guidance since November 1993. SAWS used this model to produce the basic meteorological data for the SAFARI project. The SAWS ETA model is a hydrostatic model with a horizontal grid spacing of approximately 48 km and 38 vertical levels, with layer depths that range from 20 m in the planetary boundary layer to 2 km at 50 mb. There have been several major ETA Model upgrades at SAWS: in March 1996, August 1998, November 1999, and August 2001.	proprietary
-eur11_1.0	High resolution climate data for Europe	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	-44.6418061, 21.958194, 64.9248601, 72.6081938	https://cmr.earthdata.nasa.gov/search/concepts/C2789815092-ENVIDAT.umm_json	High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.    The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.	proprietary
-european-snow-booklet_1.0	European Snow Booklet – an Inventory of Snow Measurements in Europe	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-25.9936523, 33.4693296, 67.0605469, 71.5321027	https://cmr.earthdata.nasa.gov/search/concepts/C2789815117-ENVIDAT.umm_json	The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.    For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).	proprietary
-evoltree-conference-2021-birmensdorf-switzerland_1.0	Genomics and Adaptation in Forest Ecosystems. Book of Abstracts. EvolTree Conference 2021, 14 – 17 September 2021, Birmensdorf, Switzerland	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4549656, 47.3607695, 8.4549656, 47.3607695	https://cmr.earthdata.nasa.gov/search/concepts/C2789815129-ENVIDAT.umm_json	The first EVOLTREE Conference, taking place in hybrid format (on-site and online) at WSL Birmensdorf (Switzerland) from 14-17 September, 2021, focuses on the genomics of trees and interacting species from evolutionary, demographic, and ecological perspectives.    EVOLTREE is a European network of institutions engaged in studying the evolution and functioning of forest ecosystems, in particular trees as the foundation species in forest stands. A prime topic in the face of ongoing climate change is to elucidate how trees, together with their associated organisms such as mycorrhizal fungi, respond to rapid environmental changes.    The conference includes contributions that apply innovative approaches and consider the relevance of their research in the context of biodiversity conservation through natural dynamics or silvicultural interference.	proprietary
 ewing_0	Measurements made near South Africa in 2001	OB_DAAC STAC Catalog	2001-08-06		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360233-OB_DAAC.umm_json	Measurements made near South Africa in 2001.	proprietary
-example-geodata-for-demonstrating-geospatial-preprocessing-at-foss4g2019_1.0	Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	11.3475037, 47.5904203, 11.5795898, 47.7245445	https://cmr.earthdata.nasa.gov/search/concepts/C2789815147-ENVIDAT.umm_json	This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).  The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.  The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.     Additional notes for vector data:   Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API -  https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any  perimeter or area can be digitised freely using the QGIS editing toolbar.    The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:    - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)  - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif    This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.    Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.    This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.	proprietary
-experimental-rockfall-dataset-tschamut-grisons-switzerland_1.0	Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	8.7007642, 46.6518076, 8.7037575, 46.6540464	https://cmr.earthdata.nasa.gov/search/concepts/C2789815165-ENVIDAT.umm_json	# Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.     The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of     * Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)    * Digital Elevation Model (grid resolution 2 m) obtained via UAV  * Orthophoto (5 cm resolution) obtained via UAV  * Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)  * StoneNode v1.0 raw data stream for equipped rocks.    Further information is found in    * __A. Caviezel__ et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/  * __ P. Niklaus__ et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/	proprietary
-experimental-rockfall-trilogy-of-surava_1.0	Experimental rockfall trilogy of Surava	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.5958281, 46.6544588, 9.61411, 46.6624116	https://cmr.earthdata.nasa.gov/search/concepts/C2789815192-ENVIDAT.umm_json	We performed an experimental trilogy of induced rockfall experiments in a spruce stand in Surava (CH) within (i) the original forest, (ii) after a logging job, resulting in lying deadwood and (iii) the cleared, deadwwod-free state. The three experimental set-ups allow quantifying the deadwood effect on overall rockfall risk for the same forest (slope, species) in three different conditions.	proprietary
 explorer_0	Measurements made near the Cayman Islands between 2001 and 2003	OB_DAAC STAC Catalog	2001-07-11		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360234-OB_DAAC.umm_json	Measurements made in the Caribbean Sea near the Cayman Islands between 2001 and 2003.	proprietary
 exrad3dimpacts_1	ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS	GHRC_DAAC STAC Catalog	2020-01-25	2020-02-07	-90.885, 33.2806, -71.5199, 44.726	https://cmr.earthdata.nasa.gov/search/concepts/C2645112180-GHRC_DAAC.umm_json	The ER-2 X-band Radar (EXRAD) 3D Winds IMPACTS dataset consists of horizontal wind components, uncertainties in the horizontal wind components, and radar reflectivity collected by the EXRAD instrument onboard the NASA ER-2 aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023, No deployments occurred in 2021 due to COVID-19). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The EXRAD 3D Winds IMPACTS dataset files are available from January 25 through February 7, 2020 in netCDF-3 format.	proprietary
 exradepoch_1	ER-2 X-Band Doppler Radar (EXRAD) EPOCH	GHRC_DAAC STAC Catalog	2017-08-09	2017-08-31	-124.717, 16.603, -83.6115, 34.9083	https://cmr.earthdata.nasa.gov/search/concepts/C2132312390-GHRC_DAAC.umm_json	The ER-2 X-Band Doppler Radar (EXRAD) EPOCH dataset consists of radar reflectivity and Doppler velocity estimates collected by the EXRAD onboard the AV-6 Global Hawk Unmanned Aerial Vehicle research aircraft, though traditionally this instrument is flown on the NASA ER-2 aircraft. These data were gathered during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The EXRAD EPOCH dataset files are available from August 9, 2017 through August 31, 2017 in HDF-5 format.	proprietary
@@ -16102,20 +15714,14 @@ f97068fa-c098-4521-87ec-357c6e3b6960_NA	MERIS - Water Parameters - Lake Constanc
 fa20aaa2060e40cabf5fedce7a9716d0_NA	ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2018), version 1.0	FEDEO STAC Catalog	1979-01-06	2018-05-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142587-FEDEO.umm_json	Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces; in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979 to 2018. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the  Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km.  The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.The dataset was aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.The Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2018, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.	proprietary
 fa8dc12c-b6c5-4ff4-9781-a39c8775d4fa_NA	TerraSAR-X - Spotlight Images (TerraSAR-X Spotlight)	FEDEO STAC Catalog	2007-06-15		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2207458031-FEDEO.umm_json	"This collection contains radar image products of the German national TerraSAR-X mission acquired in Spotlight mode. Spotlight imaging allows for a spatial resolution of up to 2 m at a scene size of 10 km (across swath) x 10 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space.        For more information concerning the TerraSAR-X mission, the reader is referred to:			https://www.dlr.de/content/de/missionen/terrasar-x.html"	proprietary
 faamwdat_237_1	BOREAS AFM-02 King Air 1994 Aircraft Flux and Moving Window Data	ORNL_CLOUD STAC Catalog	1994-05-25	1994-09-17	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2807614767-ORNL_CLOUD.umm_json	Contains mission information and moving window data for AFM-01 BOREAS flux aircraft runs during 1994. Contains mission information and data for AFM-02 BOREAS flux aircraft runs during 1994.	proprietary
-face-stillberg_1.0	FACE: Stillberg CO2 enrichment and soil warming study	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.867544, 46.7716544, 9.867544, 46.7716544	https://cmr.earthdata.nasa.gov/search/concepts/C2789815224-ENVIDAT.umm_json	# Background information    High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas compared to most other regions of the world. This project involved a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 until 2009, and a soil warming treatment (+4 °C) was applied from 2007 until 2012. The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research was carried out as part of this environmental change project.    # Experimental design    The experiment consisted of 40 hexagonal 1.1 m² plots, 20 with a *Pinus mugo* ssp. *uncinata* (mountain pine, evergreen) individual in the centre and 20 with a *Larix decidua* (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounded the tree in each plot, including the dominant dwarf shrub species *Vaccinium myrtillus* (bilberry), *Vaccinium gaultherioides* (group *V. uliginosum agg.*, northern bilberry) and *Empetrum nigrum* ssp. *hermaphroditum* (crowberry) plus several herbaceous and non-vascular species.  At the beginning of the experimental period, the 40 plots were assigned to ten groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment, while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.    # Data description    Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009). The data comprise of air temperature, soil temperature, soil moisture, sapflow, tree diameter and CO2 measurements.	proprietary
-factors-influencing-teenagers-forest-visit-frequency_1.0	Factors influencing teenagers' forest visit frequency	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815249-ENVIDAT.umm_json	The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.	proprietary
-factors-slowing-down-upward-shifts-of-trees-upper-elevation-limits_1.0	Factors slowing down upward shifts of trees’ upper elevation limits	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815267-ENVIDAT.umm_json	Species range limits are expected to be dramatically altered under future climate change and many species are predicted to shift their distribution upslope to track their suitable conditions (i.e. based on their niche). However, there might be large discrepancies between the speed of the upward shift of the climatic niche and the actual migration velocity of the species, especially in long-lived organisms such as trees. Here, we compared the simulations of the upslope displacement of the bioclimatic envelope of 16 tree species inhabiting temperate mountain forests under ongoing and future climate change obtained by correlative species distribution models (SDMs) to those from a dynamic forest model accounting for dispersal, competition and demography. We then partitioned the discrepancy in upslope migration velocity between the SDMs and the dynamic forest model into different components by manipulating dispersal limitation, interspecific competition and demography.     This dataset contains the calibration and evaluation data used to create the bioclimatic envelope models, the predictors for the future scenarios (raster layers) and the bioclimatic input data used in the dynamic forest models used in the following publication (Scherrer et al. 2020).     Paper Citation:  Scherrer, D., Vitasse, Y., Guisan , A., Wohlgemuth, T., & Lischke, H. (2020). Competition and demography rather than dispersal limitation slow down upward shifts of trees’ upper elevation limits in the Alps. Journal of Ecology, in press.	proprietary
 fasir_biophys_monthly_xdeg_970_1	ISLSCP II FASIR-adjusted NDVI Biophysical Parameter Fields, 1982-1998	ORNL_CLOUD STAC Catalog	1982-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784891689-ORNL_CLOUD.umm_json	The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data set and derived biophysical parameter fields were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. This multiple resolution (1/4, 1/2 and 1 degree in latitude and longitude) biophysical parameter data set contains essential variables for the calculation of photosynthesis, and the energy and water exchange between the Earth's surface (in particular of vegetation) and the lower boundary layer of the atmosphere. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is related to the light absorption and the photosynthetic capacity of vegetation. It also serves as an intermediate variable to calculate vegetation cover fraction (Vcover), total Leaf Area Index (LAI_T), green leaf area index (LAI_G), roughness length (z0), zero plane displacement (d), and snow-free albedo. The biophysical parameters were derived assuming one canopy layer. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.	proprietary
 fasir_ndvi_monthly_xdeg_972_1	ISLSCP II FASIR-adjusted NDVI, 1982-1998	ORNL_CLOUD STAC Catalog	1982-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784893177-ORNL_CLOUD.umm_json	The Fourier-Adjusted, Sensor and Solar zenith angle corrected, Interpolated, Reconstructed (FASIR) adjusted Normalized Difference Vegetation Index (NDVI) data sets were generated to provide a 17-year, satellite record of monthly changes in the photosynthetic activity of terrestrial vegetation. FASIR-NDVI data are also used in climate models and biogeochemical models to calculate photosynthesis, the exchange of CO2 between the atmosphere and the land surface, land-surface evapotranspiration and the absorption and release of energy by the land surface. There are three data files provided at spatial resolutions of 0.25, 0.5 and 1.0 degree in latitude and longitude. FASIR adjustments concentrated on reducing NDVI variations arising from atmospheric, calibration, view and illumination geometries and other effects not related to actual vegetation change.FASIR NDVI was also generated to provide inputs for computing a 17-year time series of associated biophysical parameters, provided as a separate data set in this data collection. The production of the FASIR NDVI data set and its associated biophysical parameters was funded by NASA's Land Surface Hydrology program and the Higher Education Funding Council for Wales (HEFCW) as a core component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II Data Collection.	proprietary
 fast_ice_1997_1999_1	Fast-ice Distribution in East Antarctica During 1997 and 1999 Determined Using RADARSAT Data	AU_AADC STAC Catalog	1997-11-01	1999-11-30	75, -70, 170, -55	https://cmr.earthdata.nasa.gov/search/concepts/C1214308565-AU_AADC.umm_json	An image correlation technique has been applied to RADARSAT ScanSAR images from November in 1997, and November 1999, to create the first detailed maps of fast ice around East Antarctica (75E-170E). This method is based upon searching for, and distinguishing, correlated regions of the ice-covered ocean which remain stationary, in contrast to adjacent moving pack ice. Within the overlapping longitudinal range of ~86E-150.6E, the total fast-ice area is 141,450 km2 in 1997 and 152,216 km2 in 1999. Calibrated radar backscatter data are also used to determine the distribution of two fast-ice classes based on their surface roughness characteristics.  The outer boundaries of the determined fast-ice area for November in 1997 and 1999 are contained in the data files for this record.  This work has been allocated to ASAC project 3024.	proprietary
 fast_ice_adelie_1	Landfast Sea Ice Areal Coverage and Nearest Distance off the Adelie Land Coast	AU_AADC STAC Catalog	1992-01-01	1999-12-31	133, -67, 143, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214313467-AU_AADC.umm_json	A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999.  The areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time).  While cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/).  Initial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel.  A number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996).  In addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins.  We examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002).  The effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).	proprietary
-fatal-avalanche-accidents-in-switzerland-since-1936-37_1.0	Fatal avalanche accidents in Switzerland since 1936/37	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082513-ENVIDAT.umm_json	**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.    This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one fatality. The data set commences on 01/10/1936. After the completion of a hydrological year, the new data is added.    The following information is provided:     * avalanche identifier      * date of the accident     * accuracy of the date in range of days before and after    * hydrological year (always from first of october to end of september)   * canton     * municipality     * start zone point latitude   * start zone point longitude   * start zone point accuracy (in meters)   * start zone point elevation (in meteres above sea level)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)       * forecasted avalanche danger level 1 (first danger)     * forecasted avalanche danger level 2 (second danger)   * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)    * number of dead persons      * number of caught persons     * number of fully buried persons     * activity/location of the accident party at the time of the incident	proprietary
-fatal-avalanche-accidents-switzerland-1995_1.0	Fatal avalanche accidents in Switzerland since 1995-1996	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815287-ENVIDAT.umm_json	Attention: this data is not updated after 2022 anymore. This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.    The following information is provided:       * avalanche identifier     * date of the accident    * accuracy of the date in range of days before and after    * canton    * name of the locality    * start zone of the avalanche      * coordinates (Swiss coordinate system, approximately in middle of start zone)      * accuracy of the coordinates in meters   * elevation (in meteres above sea level, app. in middle of start zone)     * slope aspect (main orientation of start zone)      * slope inclination (in degree, steepest point within start zone)      * number of dead persons     * number of caught persons    * number of fully buried persons      * forecasted avalanche danger level     * activity/location of the accident party at the time of the incident	proprietary
 fb086eaa-fbce-4a4e-a7f8-7184ecdbafe7_NA	MERIS - Water Parameters - North Sea, Monthly	FEDEO STAC Catalog	2006-01-01	2010-02-28	-6.10393, 49.9616, 11.4301, 61.9523	https://cmr.earthdata.nasa.gov/search/concepts/C2207458017-FEDEO.umm_json	The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/            This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps.	proprietary
 fb3750f5b2544403873f8788b3ed7817_NA	ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0	FEDEO STAC Catalog	1991-09-01	2016-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548142505-FEDEO.umm_json	The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project  which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.  To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.	proprietary
 fb4b4be0-a4a3-4dcd-b381-bacde381d3eb_NA	MERIS - Gap Free Leaf Area Index (LAI) - Global	FEDEO STAC Catalog	2003-01-01	2011-01-31	-180, -60, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2207458057-FEDEO.umm_json	This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.	proprietary
 fbfae06e787b4fefb4b03cba2fd04bc3_NA	ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v2.0	FEDEO STAC Catalog	2010-11-01	2017-04-30	-180, -88, 180, -50	https://cmr.earthdata.nasa.gov/search/concepts/C2548142550-FEDEO.umm_json	This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.It provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2017.  Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information.	proprietary
-fdp-grapevine-trunks-impact-xylem-phloem_1.0	Impact of the “Flavescence dorée” phytoplasma on xylem growth and phloem anomalies in trunks of ‘Chardonnay’ grapevines (Vitis vinifera)	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.9400816, 46.042408, 8.9438152, 46.04494	https://cmr.earthdata.nasa.gov/search/concepts/C2789815299-ENVIDAT.umm_json	"Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the ""Flavescence dorée"" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R.  The Flavescence dorée phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar ‘Chardonnay’ in order to verify their response to FDp infections.   In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens."	proprietary
 fe651dbef5d44248bef70906f4b3d12b_NA	ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1	FEDEO STAC Catalog	2001-01-01	2012-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143134-FEDEO.umm_json	This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SMR_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for ODIN/SMR in 2008.	proprietary
 feral_cat_macca_1	Biology of the Feral Cat, Felis catus (L.), on Macquarie Island	AU_AADC STAC Catalog	1975-12-01	1981-02-28	158.86386, -54.692, 158.94331, -54.4977	https://cmr.earthdata.nasa.gov/search/concepts/C1214308566-AU_AADC.umm_json	From the referenced paper:  Between December 1976 and February 1981, 246 cats were collected.  Overall sex ratio was in favour of males 1:0.8, and coat colour was tabby (74%), orange (26%) and black (2%).  The breeding season extended from October to March with the peak in November-December.  Mean number of embryos was 4.7 per female and evidence of females producing two litters was found.  Mortality in kittens increased as they grew older, with litters of kittens greater than 1.8 kg containing two or fewer animals.  Most cats lived in herbfield or tussock grassland, with very few if any in feldmark.  The total population was estimated at between 169 and 252 adult cats.  Observations of an adult male showed that its home range covered 41 ha, but this appeared not to be maintained during winter.  It's daytime activity varied greatly, much time being spent foraging for food.  Domestic cats Felis catus (L.) were feral on Macquarie Island by 1820, only 10 years after the island was discovered by sealers.  Their presence was soon noted by early naturalists.  Depredations by cats greatly reduced the numbers of burrow-nesting petrels and, together with the weka Gallirallus australis, cats were probably responsible for the extinction of the endemic parakeet Cyanoramphus novaezelandiae erythrotis and banded rail Rallus phillippensis before 1900.  Feral cats are common on several other subantarctic islands and have been intensively studied; the only previous study on Macquarie Island was on diet.  This study reports on other aspects of the biology of the feral cat on Macquarie Island.	proprietary
 ff4bfe39b7fe42fc993341d3cebdabb5_NA	ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v1.5	FEDEO STAC Catalog	2002-03-31	2016-06-30	-80, 60, -10, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2548143051-FEDEO.umm_json	This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space.  The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016.   This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin.  For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided.   The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.	proprietary
@@ -16138,8 +15744,6 @@ ffo_Betts_1989_gsm_99_1	Site Averaged Gravimetric Soil Moisture: 1989 (Betts)	OR
 ffo_Betts_1989_nsm_103_1	Site Averaged Neutron Soil Moisture: 1989 (Betts)	ORNL_CLOUD STAC Catalog	1989-07-07	1989-08-07	-96.61, 38.98, -96.45, 39.12	https://cmr.earthdata.nasa.gov/search/concepts/C2810664662-ORNL_CLOUD.umm_json	Site averaged product of the neutron probe soil moisture collected during the 1987-1989 FIFE experiment. Samples were averaged for each site, then averaged for each day. Only 15 days available in 89.	proprietary
 fhstmanr_386_1	BOREAS TGB-05 Fire History of Manitoba 1980 to 1991 in Raster Format	ORNL_CLOUD STAC Catalog	1980-01-01	1991-12-31	-102, 49, -89, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2808131955-ORNL_CLOUD.umm_json	Raster format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.	proprietary
 fhstmanv_387_1	BOREAS TGB-05 Fire History of Manitoba 1980 to 1991 in Vector Format	ORNL_CLOUD STAC Catalog	1980-01-01	1991-12-31	-102, 49, -89, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2813398355-ORNL_CLOUD.umm_json	Vector format data set covering the province of Manitoba and produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps.	proprietary
-fiber-bundle-model-for-snow-failure_1.0	Fiber Bundle Model for snow failure and concurrent Acoustic Emissions	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8473066, 46.8125512, 9.8473066, 46.8125512	https://cmr.earthdata.nasa.gov/search/concepts/C2789815051-ENVIDAT.umm_json	"This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.    The data underlay the figures in the publication ""Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model"" submitted for publication to ""Geophysical Research Letters""."	proprietary
-field-observations-of-snow-instabilities_1.0	Field observations of snow instabilities	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.7084808, 46.6864249, 10.0174713, 46.8979737	https://cmr.earthdata.nasa.gov/search/concepts/C2789815084-ENVIDAT.umm_json	This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.    These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.     The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.    These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.    ### Acknowlegements  Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.	proprietary
 fieldsunp_65_1	Optical Thickness Data: Ground (OTTER)	ORNL_CLOUD STAC Catalog	1990-02-22	1991-06-10	-123.95, 44.29, -121.33, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804770437-ORNL_CLOUD.umm_json	Field sunphotometer data collected on 8/13-15/90 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance	proprietary
 fieldwork_lawdome_1964_1	Field work results carried out on Law Dome and Wilkes Land, 1964	AU_AADC STAC Catalog	1964-01-01	1964-12-31	110, -70, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214313469-AU_AADC.umm_json	A collection of notes and field data collected in traverse work on Law Dome/Wilkes Land in 1964. Includes data on gravity, air pressure (barometric levelling), air temperature, wind, snow accumulation stakes, ice movement. Also includes results from S2 pit measurements.	proprietary
 fife_AF_dtrnd_nae_3_1	Aircraft Flux-Detrended: NRCC (FIFE)	ORNL_CLOUD STAC Catalog	1987-06-26	1989-10-31	-102, 37, -95, 40	https://cmr.earthdata.nasa.gov/search/concepts/C2968494372-ORNL_CLOUD.umm_json	Detrended boundary layer fluxes recorded on aircraft flights over the Konza	proprietary
@@ -16241,30 +15845,14 @@ fife_sur_refl_soilrefl_114_1	Soil Reflectance Data (FIFE)	ORNL_CLOUD STAC Catalo
 fife_sur_refl_unl_long_49_1	Longwave Radiation Data: UNL (FIFE)	ORNL_CLOUD STAC Catalog	1987-06-03	1989-08-11	-96.59, 38.98, -96.47, 39.12	https://cmr.earthdata.nasa.gov/search/concepts/C2980474531-ORNL_CLOUD.umm_json	Average incoming longwave radiation measured by University of Nebraska	proprietary
 fife_sur_refl_unl_surf_123_1	Surface Radiance Data: UNL (FIFE)	ORNL_CLOUD STAC Catalog	1987-05-30	1989-08-11	-96.59, 38.98, -96.47, 39.12	https://cmr.earthdata.nasa.gov/search/concepts/C2980692342-ORNL_CLOUD.umm_json	Canopy IR & air temperature, albedo, incoming and reflected shortwave, humidity	proprietary
 finnarp_aerosols_Not provided	Aerosol measurements at ABOA / FINNARP 2009	SCIOPS STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json	The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) 	proprietary
-fire-randomizer-first-release_1.0	fire-randomizer: first release	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	8.4545978, 47.3606372, 8.4545978, 47.3606372	https://cmr.earthdata.nasa.gov/search/concepts/C3226082141-ENVIDAT.umm_json	Tool to assess fire selectivity for topographic (e.g. alitiude, slope, aspect) or land use (forest or vegetation type, distance to infrastructures) categories with Monte Carlo simulations.	proprietary
 fire_emissions_724_1	SAFARI 2000 Fire Emission Data, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-14	2000-09-14	12, -27, 36, -14	https://cmr.earthdata.nasa.gov/search/concepts/C2788974415-ORNL_CLOUD.umm_json	As part of the SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively. During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profiles of CO2, CO, H2O, and CH4 up to 5.5 km on 6 occasions above instrumented ground sites and below the TERRA satellite and ER-2 high-flying research aircraft as well as trace gas emissions from ten African savanna fires. These measurements are the first broad characterization of the most abundant trace gases in nascent smoke from African savanna fires (i.e., including oxygen- and nitrogen-containing species).	proprietary
 fire_emissions_v4_R1_1293_4.1	Global Fire Emissions Database, Version 4.1 (GFEDv4)	ORNL_CLOUD STAC Catalog	1995-06-01	2016-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2763353619-ORNL_CLOUD.umm_json	This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region.	proprietary
 fisher_sat_1	Fisher Massif Satellite Image Map 1:100 000	AU_AADC STAC Catalog	1992-07-01	1992-07-31	66, -72, 68, -71	https://cmr.earthdata.nasa.gov/search/concepts/C1214308554-AU_AADC.umm_json	Satellite image map of Fisher Massif, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992.  The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 129-110). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
 flor_fna_Stillwl_1	Flora and fauna survey of the Stillwell Hills, 1996/97 - GIS data	AU_AADC STAC Catalog	1996-12-25	1997-01-30	59.3, -67.435, 59.6, -67.4	https://cmr.earthdata.nasa.gov/search/concepts/C1214308555-AU_AADC.umm_json	The broadscale distribution of flora (lichens, mosses, non-marine algae)and fauna (penguins, flying birds, seals)in the Stillwell Hills was mapped using GPS technology. Samples of flora were collected for taxonomic identification.  Data were recorded and catalogued in shapefiles.	proprietary
-flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0	Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	1.6699219, 46.5588603, 27.5097656, 59.7120972	https://cmr.earthdata.nasa.gov/search/concepts/C2789815113-ENVIDAT.umm_json	Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.	proprietary
 fltrepepoch_1	Flight Reports EPOCH	GHRC_DAAC STAC Catalog	2017-07-27	2017-08-31	-130, 10, -80, 40	https://cmr.earthdata.nasa.gov/search/concepts/C2175817241-GHRC_DAAC.umm_json	The Flight Reports EPOCH dataset consists of flight number, purpose of flight, and flight hours logged during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The mission reports are available from July 27, 2017 through August 31, 2017 in PDF format. 	proprietary
-flu-a-bh_1.0	Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.9451, 46.7479, 9.9451, 46.7479	https://cmr.earthdata.nasa.gov/search/concepts/C2789815125-ENVIDAT.umm_json	Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.	proprietary
 fluxnet_point_1029_1	ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites	ORNL_CLOUD STAC Catalog	1992-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785312311-ORNL_CLOUD.umm_json	This International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terrestrial ecosystem and atmosphere. 	proprietary
 foraging_trip_duration_BI_1	Adelie penguin foraging trip duration, Bechervaise Island, Mawson	AU_AADC STAC Catalog	1991-10-01	2005-02-01	62.8055, -67.5916, 62.825, -67.5861	https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json	Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92.  Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season.  Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick.  This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.  The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season.	proprietary
-forclim_4.0	ForClim	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815136-ENVIDAT.umm_json	"ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the ""GNU GENERAL PUBLIC LICENSE v3"" license.    ![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg ""ForClim structure"")"	proprietary
-forecast-avalanche-danger-level-european-alps-2011-2015_1.0	Forecast avalanche danger level European Alps 2011 - 2015	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	4.8779297, 43.2761391, 16.2597656, 48.179762	https://cmr.earthdata.nasa.gov/search/concepts/C2789815158-ENVIDAT.umm_json	This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).  For details on the data please refer to this publication.  The dataset contains the following:   - shape files for the warning regions in the Alps  - highest forecast danger level for each warning region and day	proprietary
-forecomon-proceedings_v14	Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7–9 June 2021, Birmensdorf, Switzerland	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4549183, 47.3607533, 8.4549183, 47.3607533	https://cmr.earthdata.nasa.gov/search/concepts/C2789815176-ENVIDAT.umm_json	Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings.  FORECOMON 2021 - the 9th forest ecosystem monitoring conference.  7-9 June 2021, WSL, Birmensdorf, Switzerland    The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed.    Copyright © 2021 by WSL, Birmensdorf  The authors are responsible for the content of their contribution.	proprietary
-forest-radiation-data_1.0	Shading by Trees and Fractional Snow Cover Control the Subcanopy Radiation Budget	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8737264, 46.8433152, 9.8778033, 46.8451938	https://cmr.earthdata.nasa.gov/search/concepts/C2789815272-ENVIDAT.umm_json	"This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper ""Shading by trees and fractional snow cover control the sub-canopy radiation budget"", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland."	proprietary
-forest-reserves-monitoring-in-switzerland_1.0	Forest Reserves Monitoring in Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	6.23634, 46.13293, 10.35923, 47.77037	https://cmr.earthdata.nasa.gov/search/concepts/C2789815284-ENVIDAT.umm_json	Long term monitoring of natural forests provides insights into ecological processes shaping forests without human intervention. To study natural forest dynamics, the former chair of silviculture at the Swiss Federal Institute of Technology (ETH) initiated a network of forest reserves in the late 1940's.    Since 2006, the monitoring is carried out in a cooperation project of  the chair of Forest Ecology at ETH, the Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL) and the Federal Office for the Environment (FOEN). The project relaunch led to a streamlining of the reserve network, which now contains 33 of the original reserves and 16 new reserves.     The main goal is to evaluate the effectiveness of the federal reserve policy by analysing to what extent forest reserves differ from managed forests in terms of structure, dynamics, and habitat quality.	proprietary
-forest-snow-model-fluela_1.0	Input datasets for forest snow modelling in Fluela valley, WY 2016-21	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8025513, 46.764199, 9.9838257, 46.8394031	https://cmr.earthdata.nasa.gov/search/concepts/C3226082624-ENVIDAT.umm_json	This dataset contains surface datasets (in particular canopy structure fields) and meteorological input (water years 2016-2021) required to run the snow model FSM2 over the Fluela valley. Land surface datasets are available for a 1.5x2.5km model domain at 2m spatial resolution, meteorological input at hourly resolution is provided for a point and corresponds to the location of the automatic weather station / snow measurement field 5DF in Davos. Corresponding FSM2 simulations are used and analyzed in the publication 'Canopy structure, topography and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests' by Mazzotti et al. (submitted to HESSD). This publication should be cited whenever the dataset is used.	proprietary
-forest-snow-modelling-davos-2012-2015_1.0	Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815307-ENVIDAT.umm_json	This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.  This publication must be cited when using this dataset.	proprietary
-forest-type-nfi_2018 (current)	Forest Type NFI	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815316-ENVIDAT.umm_json	Two versions of the data are currently available: 2018 and 2016.    The 2018 version presents a remote sensing-based approach for a countrywide mapping of the dominant leave type (DLT) with the two classes broadleaved and coniferous in Switzerland. The spatial resolution is 10 m with the fraction of the class broadleaf. The classification approach incorporates a random forest classifier, explanatory variables from multispectral Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the tree type map with independent NFI data revealed deviations in mixed stands.    In the 2016 version (3 m spatial resolution), the classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of 3.17%).	proprietary
-forest_area-44_1.0	Forest area	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815205-ENVIDAT.umm_json	The forest area is the total sum of all areas classified as forest according to NFI’s forest definition. The forest definition includes shrub forest.  This theme is also used to assess the total area when forest and non-forest need to be distinguished.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-forest_area_by_forest_function-262_1.0	Forest area by forest function	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815235-ENVIDAT.umm_json	The forest area refers to all areas classified as forest according to NFI’s forest definition. The forest definition includes shrub forest.   For each forest function (including no special forest function) identified in the survey of the forestry services, the size of the associated forest area is displayed. One forest region may fulfil several different forest functions and may thus contribute to the forest area for several forest functions.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-forest_area_by_natural_hazard-260_1.0	Forest area by natural hazard	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815257-ENVIDAT.umm_json	For each natural hazard process according to FOEN’s SilvaProtectCH, the size of the forest area affected is given. One forest region may be affected by several different natural hazard processes and may thus contribute to the forest area affected by several different natural hazard processes.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 forest_carbon_flux_949_1	Global Forest Ecosystem Structure and Function Data For Carbon Balance Research	ORNL_CLOUD STAC Catalog	1897-01-01	2006-12-31	-159.5, -42.87, 172.75, 67.36	https://cmr.earthdata.nasa.gov/search/concepts/C2784890927-ORNL_CLOUD.umm_json	A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales. The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.	proprietary
-forhycs-v-1-0-0-model-code_1.0.0	FORHYCS v. 1.0.0 model code	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815053-ENVIDAT.umm_json	Model code, technical documentation and auxiliary files for the dynamic ecohydrological model FORHYCS (FORests and HYdrology under Climate change in Switzerland). FORHYCS combines two pre-existing models, the hydrological model PREVAH and the forest landscape model TreeMig.     License: GPL v3	proprietary
-four-years-of-daily-stable-water-isotope-data_1.0	Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.6654663, 47.0120984, 8.7574768, 47.1523693	https://cmr.earthdata.nasa.gov/search/concepts/C2789815097-ENVIDAT.umm_json	This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.	proprietary
 fram25k_1	Framnes Mountains 1:25000 Topographic GIS Dataset	AU_AADC STAC Catalog	1996-03-18	1996-04-01	62.12, -68, 63.08, -67.67	https://cmr.earthdata.nasa.gov/search/concepts/C1214308559-AU_AADC.umm_json	Digital Photogrammetric Map Data of the Framnes Mountain Region taken from 1:45000 1996/97 aerial photography.  Data Layers consist of building, contour, geology (erratics only), human, spot_height, survey, topoline, topopoly and toposurf.	proprietary
 framnes_contours_1	Framnes Mountains contours smoothed and edited.	AU_AADC STAC Catalog	2003-04-01	2003-05-01	61.58, -68.17, 64.07, -67.37	https://cmr.earthdata.nasa.gov/search/concepts/C1214308580-AU_AADC.umm_json	Mapping around the Framnes Mountains from Spot satellite imagery at 10 metre pixel resolution. Mapped early in 1999.  Smoothed contour data edited May 2003.	proprietary
 framnes_route_gis_1	Mawson Area Routes GIS Dataset	AU_AADC STAC Catalog	1988-10-30	2014-04-04	59.417, -68.1, 68.983, -67.333	https://cmr.earthdata.nasa.gov/search/concepts/C1214308582-AU_AADC.umm_json	This dataset is GIS data representing waypoints and routes in the Mawson area, Antarctica. It includes routes in the Framnes Mountains and routes west and east of Mawson along the Mawson Coast. The waypoint and route data held by the Australian Antarctic Data Centre are routinely updated using feedback provided by the Australian Antarctic Division's Field Training Officers and Station Leaders with approval for changes given by the Australian Antarctic Division's Field Support Coordinator.	proprietary
@@ -16274,7 +15862,6 @@ frazier_sgp_12dec2011_1	Census of southern giant petrel nesting areas on Frazier
 frazier_sgp_14dec2005_1	Census of southern giant petrel nesting areas on Frazier Islands 14 December 2005	AU_AADC STAC Catalog	2005-12-14	2005-12-14	110.144, -66.236, 110.2, -66.221	https://cmr.earthdata.nasa.gov/search/concepts/C1214308586-AU_AADC.umm_json	Dr Eric Woehler and Phillippa Bricher, postgraduate student at the University of Tasmania, and Marty Benavente, Field Training Officer, visited the Frazier Islands on 14 December 2005. The purpose of the visit was to conduct a census of the southern giant petrels on the islands. This dataset includes the counts and GIS polygon data representing the extents of the southern giant petrel nesting areas and adelie penguin colonies observed on this visit.	proprietary
 fuel_evaporation_1	Investigation of evaporation and biodegradation of fuel spills in Antarctica - a chemical approach using GC-FID	AU_AADC STAC Catalog	2003-11-01	2003-11-30	60, -70, 160, -53	https://cmr.earthdata.nasa.gov/search/concepts/C1214313454-AU_AADC.umm_json	Evaporation Model for hydrocarbon spills.    Developed by the Australian Antarctic Division to simulate fractionation of Special Antarctic Blend (SAB) and other diesel range fuels during evaporation.  Text version of notes for excel model, please read me.  This Package of files includes a pdf of the scientific paper, a readme word document and 3 excel files.  The purpose of the 3 excel files are as follows:  Excel File 1.   Evap Model_V1_single temperature.xls.   Evaporation predictions with a single sample at one temperature.  This file requires input of initial composition, composition after weathering and a single temperature.  Excel File 2.   Evap Model_V1_five temperatures.xls.  As above but with the input of 5 different temperatures.  Excel File 3.   Evap Model_development version, derivation of data and AAD examples.xls   This is an earlier development version of the numerical model. This file is intended to allow others to see how available Antoines equation parameters were originally fitted for use with other R+UCM regions.  This file includes a worksheet where the fitting parameters for Antoines eqn were calculated, a necessary task to be able to extrapolate to compounds for which explicit vapour pressure data are not available. The data input sheet has some differences to the singe temperature and five temperature model as the R+UCM regions are split up differently.  Also the assumptions about how the different classes of compound (i.e. the Aliphatic and aromatic classes) behave can be altered.   Some raw data from the AAD evaporation experiment is included and plotted.  The Temperature comparisons worksheet summarises how ratios of interest change and allows plotting of one ratio at different temperatures (see worksheet Figs 2 and 3).  Read me about background to this excel file Background Notes  This excel file estimates the relative evaporation rates of different hydrocarbons from a hydrocarbon mixture (i.e. a fuel).  In this excel file, a single temperature is considered for an evaporating hydrocarbon mixture.  The desired temperature and details about the hydrocarbon mixture are entered in the Main Input page.  The model was developed for use with the bulk fuels used by the Australian Antarctic Division at Casey, Davis, Mawson and Macquarie Island.  This fuel, Special Antarctic Blend (SAB) starts at C9.  As some of the spill sites undergoing remediation are mixed with heavier lube range hydrocarbons the options for inputs go to C36 - a typical maximum when Total Petroleum Hydrocarbon analysis is undertaken.  The model uses thermodynamic data where such data are available.  Estimated thermodynamic data are used for components when specific data are unavailable.  Other important information can be found in the sections listed below. References GC-FID data used in the model Temperature and Vapour pressure estimation Other corrections How the calculations are done Experimental data (that supports the approach taken in this excel file)  Read me about References Notes about the scientific publication that this model is reported in.  Paper can be obtained as a pdf file from the Australian Antarctic Data Centre.  The paper contains details on the evaporation experiments at +20 degrees C and -20 degrees C.  The results from the experiments agree well with the calculated fractionation rates that this Excel file produces.  Read me about GC-FID data for this model Notes about GC-FID data used in this excel model.  This model was set up to directly use GC-FID data outputs for each of the compounds of GC regions listed.  Consistent units are required for the areas of each measured region (and GC bias needs to be low across the range of fuel components).  Note that the summation of all the regions and compounds = total area identified in the chromatogram from C9 up to C36.  This range covers the observed range of components in SAB, Arctic Blend Diesel and lubes that may have been spilled in the same area.  Read me about temperature / vapour pressure estimation Notes about Temperature correction method  Raoults law and the Antoine equation are used to calculate the composition of the evaporating portion of the fuel mixture.  Vapour pressure data were obtained for a range of available hydrocarbons C9 and larger in the temperature ranges that covered the site temperature ranges, approx -20 degrees C to +20 degrees C.  The available data were mostly limited to n-alkanes.  This situation was exacerbated because other compounds of interest are solids at these temperatures when pure.  Consequently vapour pressure data are not available for these components in the liquid form at these temperatures.    The available n-alkane vapour pressure data were combined and a best fit of these data were determined as a function of effective Carbon Number (ECN).  This allowed the estimation of the vapour pressure of fuel components with ECN's between the n-alkane ECN's.  As a refinement each region of the chromatogram was split into 5 classes - 3 x aliphatic fractions and 2 x aromatic fractions.  The ECN used to estimate Vapour pressure of each class was slightly modified from the average retention time relative to an n-alkane.  This modification was carried out to correct for specific GC-column / compound interactions - interactions that increase from acyclic to cyclic to polycyclic to hindered aromatics [i.e. multiply alkylated] to unhindered aromatics. Examination of the evaporation behaviour of n-alkanes vs. the nearest ECN regions confirms the need for the correction with the calculated evaporation profiles better matching the observed profiles.  When an n-alkane's evaporation rate is compared to a GC region with distinctly different vapour pressures these corrections to ECN make little or no difference to the predicted selectivity during evaporation.  Read me about other corrections Notes about other corrections used in this model.  This model does not include systems that are under diffusional control (i.e. limited by diffusion of hydrocarbons within the soil / fuel mixture).  The assumptions in the model are for an evenly mixed liquid that is evaporating.  The experiment was set up to avoid this problem (by rotating the flasks).  Soil with evaporating fuels may well be affected by this and other problems.  Read me about how calculations are done Notes about how the calculation is done  The fuel is divided into a number of fuel classes and specific compounds are identified.  These classes are listed in the Main Input Page ready for use with the example data or other fuel data in the appropriate fuel range.  R+UCM stands for Resolved + Unresolved Complex Mix but needs to be calculated excluding the specifically identified compounds like n-alkanes.  Specifically identified compounds are excluded from the R+UCM so they are not double counted.  Mol fraction of each component is estimated.  For specific compounds an exact molecular mass is known.  For other R+UCM classes molecular mass is estimated from known compounds in that region.    Vapour pressure of the pure component or region is estimated with Antoines equation for the temperature required.  This pure vapour pressure estimate is combined with liquid phase mol fraction to calculate the gas phase composition at each evaporation step.  The evaporating portion (i.e. the gas phase portion) is removed from the liquid phase portion.  The Excel sheet is setup such that this subtraction accounts for approximately 1% of the initial fuel amount.  The need to calculated mol fractions then back-calculated mass remaining is the reason it is not exactly 1% by mass at each step.  To avoid numerical errors, division by zero errors and rounding errors many of the calculations contain and the IF formula.  When a fuel component is greater than 0 the mol fractions are calculated, otherwise a value of 0 is returned.  Read me about AAD experimental data Notes about AAD experiments that back up the model (a full description is in the scientific publication)  Small portions of Special Antarctic Blend (SAB) fuel were placed into vials. Each vial was placed into a +20 degrees C or -20 degrees C chamber. A slow stream of nitrogen passed into the top of each vial to remove the evaporating portion of the fuel.  The vials were slowly rotated to ensure even mixing of the residual fuel during rotation.  Periodically a vial was removed, weighed to calculate mass fuel evaporated, and analysed with GC-FID apparatus.  After a range of vials were analysed at different levels of evaporation at the 2 temperatures a data set was obtained to validate the numerical model.	proprietary
 fuel_load_755_1	SAFARI 2000 Modeled Fuel Load in Southern Africa, 1999-2000	ORNL_CLOUD STAC Catalog	1999-09-01	2000-08-31	5, -34.99, 42.49, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2789031212-ORNL_CLOUD.umm_json	This data set contains global, spatially explicit (1 km2 grid cells) and temporally explicit (semi-monthly) modeled output of fuel loads over southern Africa. The fuel types considered in the data set are litter (dead tree leaves), dead grass, green grass, and small-diameter twigs. The Production Efficiency Model (PEM) was used to produce the estimated fuel loads for southern Africa for the 1999-2000 growing seasons.	proprietary
-full-content-of-wsl-fauna-database_1.0	Full content of WSL Fauna Database	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082225-ENVIDAT.umm_json	Complete extract of Fauna Database of WSL, containing all projects and all taxa. Meant as exchange and citation platform for sharing the data with the national data centre 'Centre Suisse de la Cartographie de la Fauna (CSCF)', and Info Fauna.	proprietary
 g3acld_003	SAGE III Meteor-3M L2 Monthly Cloud Presence Data (HDF-EOS) V003	LARC STAC Catalog	2001-12-10		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C182161244-LARC.umm_json	A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules	proprietary
 g3acldb_003	SAGE III Meteor-3M L2 Monthly Cloud Presence Data (Native) V003	LARC STAC Catalog	2001-12-10		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C182161250-LARC.umm_json	A monthly data file coincident with solar event granules, that provides information about cloud presence during data capture of the granules	proprietary
 g3alsp_003	SAGE III Meteor-3M L2 Lunar Event Species Profiles (HDF-EOS) V003	LARC STAC Catalog	2001-12-10		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C184964538-LARC.umm_json	A Level 2 data file containing all the species products for a single lunar event	proprietary
@@ -16305,13 +15892,7 @@ g3btmnc_52	SAGE III/ISS L1B Monthly Solar Event Transmission Data (NetCDF) V052
 g3btmnc_53	SAGE III/ISS L1B Monthly Solar Event Transmission Data (NetCDF) V053	LARC STAC Catalog	2017-05-31		180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2625163351-LARC.umm_json	Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process.    g3btmnc_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1 Monthly Solar Event Species Profiles (NetCDF) V053 data product. It contains pixel group transmission profiles for a month of solar events.    Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.	proprietary
 gap_filled_marconi_811_1	FLUXNET Marconi Conference Gap-Filled Flux and Meteorology Data, 1992-2000	ORNL_CLOUD STAC Catalog	1992-01-01	2000-12-31	-157.41, -2.61, 24.3, 70.47	https://cmr.earthdata.nasa.gov/search/concepts/C2776899492-ORNL_CLOUD.umm_json	Fluxes of carbon dioxide, water vapor, and energy exchange have been measured at 38 forest, grassland, and crop sites as part of the EUROFLUX and AmeriFlux projects. A total of 97 site-years of data were compiled, primarily between 1996 and 1998 but also for 1992-1995 and 1999-2000. Half-hour flux and meteorology measurements are included plus the gap-filled half-hour estimates and aggregations to day and night, weekly, monthly, and annual periods. The FLUXNET 2000 Synthesis Workshop was held at the Marconi Conference Center, Marshall, California, June 11-14, 2000. The Marconi Flux Data Collection was compiled to aid in exploring the interactions between the terrestrial biosphere and the overlying atmosphere through carbon, water, and energy exchanges. The workshop resulted in several studies to synthesize and interpret differences and similarities in long-term measurements of carbon dioxide, water vapor, and energy exchanges between vegetation and the atmosphere for a spectrum of ecosystems. A series of synthesis papers based on these data and studies was published in a special issue of the Agriculture and Forest Meteorology, Volume 113, 2002. The papers are listed in the reference section. This data product is being archived as a record of the data used the AFM special issue. Updates and revisions to the data are available at the FLUXNET web site.The eddy covariance technique is used for long-term continuous measurements of mass and energy fluxes to capture seasonal dynamics and allow for a meaningful scaling with respect to time. The equipment and methodology were standardized among sites by using common software and instrumentation. Comparisons of ecosystem fluxes among sites are usually performed on annual or monthly sums calculated on complete data records; however, the average site data coverage during a year was only 65%. Therefore, development and application of robust and consistent data gap-filling methods was required before fluxes could be calculated. One of the outcomes of the FLUXNET project was computer applications to process the data into complete, consistent, quality assured, and documented data sets (Falge et al. 2001a,b). Gap-filled flux data from four different filling methods are reported. Selected meteorological parameters were also gap filled to support flux estimating methods and are reported along with non-filled meteorological data. Note that the measured/estimated CO2 fluxes and storage fluxes were summed into net ecosystem exchange (NEE), and ONLY NEE data are reported. 	proprietary
 gaz_1	Australian Antarctic Gazetteer	AU_AADC STAC Catalog	1952-01-01		40, -90, 160, -53	https://cmr.earthdata.nasa.gov/search/concepts/C1214308588-AU_AADC.umm_json	The Australian Antarctic Gazetteer is maintained by the Australian Antarctic Data Centre  and the Secretary of the Australian Antarctic Division Place Names Committee. It contains information about names in the Australian Antarctic Territory and the Territory of Heard Island and McDonald Islands. Users can search by place name, region, feature type, latitude or longitude. Displayed information includes a descriptive narrative, and where available, an image, source information and altitude.  Users can download the whole gazetteer or their search results as a KML or CSV file.	proprietary
-gbif-range-r_0.2	gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3226082333-ENVIDAT.umm_json	Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)	proprietary
-gcnet_1.0	Greenland Climate Network (GC-Net) Data	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	-69.2578125, 58.9288406, -10.1953125, 83.2212265	https://cmr.earthdata.nasa.gov/search/concepts/C2789815145-ENVIDAT.umm_json	"## In Memory of Dr. Konrad (Koni) Steffen    <br /> <br />  Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).  Starting October 3, 2022, the access to the latest versions of the ""ready to use"" L1 data has been migrated to GEUS. Future data versions will be available at:  [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)  ### Background   Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was ""Swiss Camp"" or the ""ETH-CU"" camp (GC-Net station #01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see ""C-file"" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.    ### Overview   Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.    ### The standard GC-Net station consists of:   * Air temperature measurements at 2 heights above the surface * Temperature and humidity measurements at 2 heights above the surface * Wind speed and direction measured at 2 heights above the surface * Sonic distance sounder measurements for 2 snow height and distance of instruments to surface * Incoming shortwave radiation measurement * Reflected shortwave radiation measurement * Net broadband radiation (long- and short-wave) measurement * Air pressure measurement  Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.    ### Dataset   This dataset consists of 2 main data levels; Level 0 and Level 1.  Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:   * “C file” - contains the historical processed datafile for each station.  * “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).  * “Photos” - contains photographs of the station when available marked by year.  Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):  [GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing ""GC-Net-level-1-data-processing"")  Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead ""NEAD format"").  <br />  ### Additional Details   Dataset description publication will be forthcoming.  The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future."	proprietary
-gcos-swe-data_1	GCOS SWE data from 11 stations in Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	7.7511649, 46.0234031, 10.4193435, 46.8605795	https://cmr.earthdata.nasa.gov/search/concepts/C2789815162-ENVIDAT.umm_json	This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station. The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes: • Raw data are archived in the snow and avalanche database at SLF. • Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation. • Historical data of these 11 stations have been digitized and all data have been quality controlled.  • Detailed metadata (location of measurements) are available. • Data gaps for the two most important winter and spring dates were reconstructed based on a published SWE parameterization from co-located snow depth measurements. • Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).	proprietary
 gdp_xdeg_974_1	ISLSCP II Global Gridded Gross Domestic Product (GDP), 1990	ORNL_CLOUD STAC Catalog	1990-01-01	1990-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784894332-ORNL_CLOUD.umm_json	The data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990.Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects.  This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN. 	proprietary
-gem-bh_1.0	Processed permafrost borehole data (2940 m asl), Gemsstock, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	8.61026, 46.60097, 8.61026, 46.60097	https://cmr.earthdata.nasa.gov/search/concepts/C2789815206-ENVIDAT.umm_json	Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50&deg;) North-West slope (315&deg;). The surface material is bedrock and borehole depth is 40 m.  Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.    __Publications__    1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579–597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101.    2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157–164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.	proprietary
-gem2_1.0	GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	8.60904, 46.60369, 8.60904, 46.60369	https://cmr.earthdata.nasa.gov/search/concepts/C2789815190-ENVIDAT.umm_json	Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.    Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites	proprietary
-generalised-stand-descriptions-within-the-swiss-nfi_1.0	Generalised stand descriptions in Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815225-ENVIDAT.umm_json	"The files refer to the data and R code used in Mey et al. ""From small forest samples to generalised uni- and bimodal stand descriptions"" (2021) _Methods in Ecology and Evolution_.    __Generalised stand descriptions__ are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data.    The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (NFI). The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).    ---------------------------------------    The file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper.    The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.    The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data."	proprietary
 geocoord_556_1	BOREAS Site and Area Geographic Coordinate Information	ORNL_CLOUD STAC Catalog	1992-01-01	1997-12-31	-111, 49, -88, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2808093396-ORNL_CLOUD.umm_json	Geographic coordinate and other site information from several sources throughout the experiment period. The final set of information is organized into two data sets that provide geographic coordinate and site characteristic information for single sites and corner coordinates for standard geographic areas.	proprietary
 geodata_0001_Not provided	Cereals - Production	CEOS_EXTRA STAC Catalog	1961-01-01	2009-12-31	-180, -90, 180, -60.5033	https://cmr.earthdata.nasa.gov/search/concepts/C2232846694-CEOS_EXTRA.umm_json	Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption).  When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T).	proprietary
 geodata_0028_Not provided	Improved Sanitation Coverage - Rural Population	CEOS_EXTRA STAC Catalog	1990-01-01	2008-12-31	-180, -90, 180, -60.5033	https://cmr.earthdata.nasa.gov/search/concepts/C2232846604-CEOS_EXTRA.umm_json	Access to safe drinking water is estimated by the percentage of the population using improved drinking water sources, as described below. Similarly, access to sanitary means of excreta disposal is estimated by the percentage of the population using improved sanitation facilities. Improved sanitation facilities are those more likely to ensure privacy and hygienic use. Improved drinking water technologies are those more likely to provide safe drinking water than those characterized as unimproved.  Improved drinking water sources comprise: Household connection; Public standpipe; Borehole; Protected dug well; Protected spring; Rainwater collection.	proprietary
@@ -16638,9 +16219,7 @@ gis38_1	Davis Station - Data eyed in based on advice from AAD Atmospheric Scienc
 gis41_1	Cape Denison and McKellar Islands GIS dataset from Ikonos satellite imagery	AU_AADC STAC Catalog	2001-01-26	2001-01-31	142.5, -67.1, 143.1, -66.9	https://cmr.earthdata.nasa.gov/search/concepts/C1214313498-AU_AADC.umm_json	A GIS dataset of around Cape Denison and part of George V land created from two IKONOS satellite images. Layers created from digitising directly from the imagery include: mapping extent, continent, building, refuge, coastline, reef, offshore rocks, sea, snow, sheet, island, birds, rock, moraine, sea ice, lakes - The mapping extent layer represents the edge of the IKONOS imagery. - The continent layer represents the land mass shown in IKONOS imagery. It was generated using the digitised coastline and bounded by lines that represent the edge of the image. - The snow spatial data represents the snow cover in January 2001 - The sheet ice spatial data represents the ice extent in January 2001 - The penguin spatial data represents the penguin colony extents, based on guano deposits. - The rock spatial data represents the exposed bare rock	proprietary
 gis43_1	Bechervaise Island - Total station survey, February 2002	AU_AADC STAC Catalog	2002-02-22	2002-02-22	62.8084, -67.5879, 62.8152, -67.5863	https://cmr.earthdata.nasa.gov/search/concepts/C1214313499-AU_AADC.umm_json	This dataset represents the refuge, infrastructure and penguin colony markers on Bechervaise Island, Holme Bay, Antarctica. The data were derived from a total station survey by Aaron Read on 22 February 2002.	proprietary
 gis45_1	Mawson Station - update of station data 2004	AU_AADC STAC Catalog	2004-02-01	2004-02-01	62.8611, -67.6083, 62.8889, -67.5944	https://cmr.earthdata.nasa.gov/search/concepts/C1214313500-AU_AADC.umm_json	The Australian Antarctic Data Centre's GIS data of Mawson Station was updated in 2004 using a map image provided by Dr Malcolm Arnold who wintered at the station during that year.  The data are included in the data available for download from a Related URL below.  The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.  Data described by this metadata record has Dataset_id = 45.  Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.	proprietary
-gisdata_1.0	Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789815253-ENVIDAT.umm_json	"The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the ""Cross Blended Hypso with Shaded Relief and Water"". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.    Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com"	proprietary
 giss_wetlands_632_1	SAFARI 2000 Wetlands Data Set, 1-Deg (Matthews and Fung)	ORNL_CLOUD STAC Catalog	1971-01-01	1982-01-01	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2796830663-ORNL_CLOUD.umm_json	This database provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. The subset retains all five arrays at the 1-degree resolution but only for the area of interest. The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type.	proprietary
-gl_microclim_1.0	Greenland shrubs and microclimate	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-52.35655, 64.03434, -49.71769, 65.01146	https://cmr.earthdata.nasa.gov/search/concepts/C2789815278-ENVIDAT.umm_json	## Study Aim    We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.    ## Data Description    Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.     ### Nuup Kangerlua (Godthåbsfjord)    _Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness    ### Kangerluarsunnguaq (Kobbefjord)    Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height       ## Methods    ### Field survey in Nuup Kangerlua    We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).     ### Field survey in Kangerluarsunnguaq     We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).    ### Biotic variables    We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.    ### Climate variables     We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).    ### Topography and terrain wetness indicator variables    We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.     ### Computer code    Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)    ### Third-party data    Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).	proprietary
 glacio_1973_barometric_levelling_1	Barometric Levelling over IAGP trilateration net, Wilkes Land 1973	AU_AADC STAC Catalog	1973-01-01	1973-12-31	110, -70, 120, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214313503-AU_AADC.umm_json	A collection of tabulated values of atmospheric pressure along routes traveled during traverse for use in barometric leveling.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 glacio_1981_traverse_data_report_1	Glaciology 1981 Traverse Data Report (inland from Casey) - M. Hendy	AU_AADC STAC Catalog	1981-01-01	1981-12-31	110, -70, 128, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214308596-AU_AADC.umm_json	A report on the four oversnow traverses carried out in 1981 from Casey, inland to Law Dome and Wilkes Land. Includes copy of the data collected for accumulation and density measurements, barometric profiling, gravity, ice thickness and bedrock profiling, and snow temperature, surface density and oxygen isotope measurements.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 glacio_78_iagp_data_casey_1	IAGP Data for 1978 From Casey	AU_AADC STAC Catalog	1978-01-01	1978-12-31	110, -74, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214308571-AU_AADC.umm_json	A collection of data/observations recorded during the 1978 IAGP traverse from Casey. Included in the collection are accumulation stake height readings, barometric levelling observations, precise distance calculations between canes in the ice movement network (along with the resulting ice velocity calculation results), and an instrumentation report.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
@@ -16657,12 +16236,9 @@ glacio_data_report_1981_casey_1	Glaciology Data Report, Casey 1981	AU_AADC STAC
 glacio_data_report_1982_casey_1	Glaciology Data Report, Casey 1982	AU_AADC STAC Catalog	1982-01-01	1982-12-31	105.11719, -69.71811, 117.07031, -65.94647	https://cmr.earthdata.nasa.gov/search/concepts/C1278304362-AU_AADC.umm_json	A report presenting the data collected during the 1982 ANARE Glaciology program at Casey, resulting from several inland traverses.  Measurements recorded include ice movement, barometric levelling, bedrock profiling, accumulation and gravity.  Fieldwork locations were Casey, Law Dome and Wilkes Land.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 glacio_data_report_1983_casey_1	Glaciology Data Report, Casey 1983	AU_AADC STAC Catalog	1983-01-01	1983-12-31	110, -74, 115, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214308599-AU_AADC.umm_json	A report presenting the data collected during the 1983 ANARE Glaciology program at Casey, resulting from five inland traverses. Measurements made include ice movement, barometric levelling, bedrock profiling, accumulation, gravity, magnetic, surface wind, 10m temperatures, stratigraphy measurements and isotope sampling, along with traverse notes.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 glacio_data_report_1986_casey_1	Glaciology Data Report, Casey 1986	AU_AADC STAC Catalog	1986-01-01	1986-12-01	110, -69, 115, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214308600-AU_AADC.umm_json	A report of the data collected from the 1986 Glaciology program at Casey. Includes measurements of ice movement, accumulation, snow temperature, gravity, magnetic, weather data, surface density and hardness, and a summary of all known measurements along the A, B and Undulation Lines on Law Dome.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
-glide-snow-avalanche-activity-on-dorfberg-davos_1.0	Glide-snow avalanche activity on Dorfberg, Davos, Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.8270988, 46.8077793, 9.8497581, 46.8265749	https://cmr.earthdata.nasa.gov/search/concepts/C3226082548-ENVIDAT.umm_json	This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:    Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91    We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.	proprietary
 glider_0	Glider measurements near Tampa, FL	OB_DAAC STAC Catalog	2009-02-06		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360244-OB_DAAC.umm_json	Measurements made near Tampa along the Florida Gulf Coast to calibrate and validate glider instrumentation between 2009 and 2011.	proprietary
 glmcierra_1	Geostationary Lightning Mapper (GLM) Cluster Integrity, Exception Resolution, and Reclustering Algorithm (CIERRA)	GHRC_DAAC STAC Catalog	2017-01-12	2023-03-31	-180, -57.312, 180, 57.267	https://cmr.earthdata.nasa.gov/search/concepts/C3160666934-GHRC_DAAC.umm_json	The Geostationary Lightning Mapper (GLM) Cluster Integrity, Exception Resolution, and Reclustering Algorithm (CIERRA) dataset consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, series, flashes, and areas. The GLM CIERRA data addresses the artificial flash termination by the GLM ground system by recombining split flashes and filtering out more non-lightning noise. This provides researchers with a powerful tool to better investigate convective storm and lightning activity with more accurate observations as well as better incorporate spatial extent observations that can be used for aviation meteorology, lightning safety, and other studies. These data are available from January 12, 2017, through March 31, 2023, in netCDF-4 format.	proprietary
 glmgoesL3_1	GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products	GHRC_DAAC STAC Catalog	2017-12-18		162.9, -57, -76.2, 57	https://cmr.earthdata.nasa.gov/search/concepts/C2278812167-GHRC_DAAC.umm_json	The GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products consist of full disk extent gridded lightning flash data collected by the Geostationary Lightning Mapper (GLM) onboard the Geostationary Operational Environmental Satellite 16 and 17 (GOES-16 and GOES-17). These satellites are a part of the GOES-R series program: a four satellite series within the National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Association (NOAA) GOES program. GLM is the first operational geostationary optical lightning detector that provides total lightning data (in-cloud, cloud-to-cloud, and cloud-to-ground flashes). While it detects each of these types of lightning, the GLM is unable to distinguish between each type. The GLM GOES L3 dataset files contain gridded lightning flash data over the Western Hemisphere in netCDF-4 format from December 31, 2017 to present as this is an ongoing dataset.	proprietary
-global-cryosphere-watch-data-survey_1.0	Global Cryosphere Watch data survey	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815290-ENVIDAT.umm_json	Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).	proprietary
-global-species-distributions-for-mammals-reptiles-and-amphibians_1.0	Global species distributions for mammals, reptiles, and amphibians	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C3226082087-ENVIDAT.umm_json	We modelled the global distribution of 730 amphibian, 1276 reptile, and 1961 mammal species globally as a function of current climate at a 0.5° spatial resolution using four different predictor groups composed of different combinations of input variables: mean climatic conditions, spatial climatic variability, and temporal (interannual) climatic variability.	proprietary
 global_N_cycle_797_1	Global N Cycle: Fluxes and N2O Mixing Ratios Originating from Human Activity	ORNL_CLOUD STAC Catalog	1756-01-01	2004-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776893351-ORNL_CLOUD.umm_json	Nitrogen is a major nutrient in terrestrial ecosystems and an important catalyst in tropospheric photochemistry. Over the last century human activities have dramatically increased inputs of reactive nitrogen (Nr, the combination of oxidized, reduced and organically bound nitrogen) to the Earth system. Nitrogen cycle perturbations have compromised air quality and human health, acidified ecosystems, and degraded and eutrophied  lakes and coastal estuaries [Vitousek et al., 1997a, 1997b; Rabalais, 2002; Howarth et al., 2003; Townsend et al., 2003; Galloway et al., 2004]. To begin to quantify the changes to the global N cycle, we have assembled key flux data and N2O mixing ratios from various sources.  The data assembled from different sources includes fertilizer production from 1920-2004;  manure production from 1860-2004; crop N fixation estimated for three time points, 1860, 1900, 1995; tropospheric N2O mixing ratios from ice core and firn measurements, and tropospheric concentrations to cover the time period from 1756-2004.  The changing N2O concentrations provide an independent index of changes to the global N cycle, in much the same way that changing carbon dioxide concentrations provide an important constraint on the global carbon cycle.  The changes to the global N cycle are driven by industrialization, as indicated by fossil fuel NOx emission, and by the intensification of agriculture, as indicted by fertilizer and manure production and crop N2 fixation. The data set and the science it reflects are by nature interdisciplinary.  Making the data set available through the ORNL DAAC is an attempt to make the data set available to the considerable interdisciplinary community studying the N cycle.	proprietary
 global_N_deposition_maps_830_1	Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050	ORNL_CLOUD STAC Catalog	1860-01-01	2050-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776896954-ORNL_CLOUD.umm_json	This data set provides global gridded estimates of atmospheric deposition of total inorganic nitrogen (N), NHx (NH3 and NH4+), and NOy (all oxidized forms of nitrogen other than N2O), in mg N/m2/year, for the years 1860 and 1993 and projections for the year 2050. The data set was generated using a global three-dimensional chemistry-transport model (TM3) with a spatial resolution of 5 degrees longitude by 3.75 degrees latitude (Jeuken et al., 2001; Lelieveld and Dentener, 2000). Nitrogen emissions estimates (Van Aardenne et al., 2001) and projection scenario data (IPCC, 1996; 2000) were used as input to the model.	proprietary
 global_population_xdeg_975_1	ISLSCP II Global Population of the World	ORNL_CLOUD STAC Catalog	1990-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784894945-ORNL_CLOUD.umm_json	Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:    * Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.    * Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.    * Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.    * The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.    * Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.	proprietary
@@ -16706,7 +16282,6 @@ gomc_162_Not provided	Circulation and Contaminant Transport in Massachusetts Coa
 gomc_219_Not provided	2001 Long Island Sound Study Ambient Water Quality and Monitoring Program	SCIOPS STAC Catalog	1970-01-01		-74.3, 40.5, -71.75, 41.5	https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json	 The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941.  Waterbody or Watershed Names: Long Island Sound  	proprietary
 gomc_323_Not provided	ACAP Saint John's Community Environmental Monitoring Program (CEMP)	SCIOPS STAC Catalog	1992-01-01		-66.25, 45, -65.25, 46	https://cmr.earthdata.nasa.gov/search/concepts/C1214585928-SCIOPS.umm_json	 Parameters measured included: ammonia nitrogen, orthophosphate,       dissolved oxygen, pH, turbidity, salinity, faecal coliform.              	proprietary
 gomc_40_Not provided	Air Quality Monitoring In New Brunswick	SCIOPS STAC Catalog	1970-01-01		-145.27, 37.3, -48.11, 87.61	https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json	 We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors.  Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment.  	proprietary
-gone-wild-grapevines-in-forests_1.0	Gone-wild grapevines in forests may act as a potential habitat for “Flavescence dorée” phytoplasma vectors and inoculum	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.4347534, 45.8809865, 9.2422485, 46.5159373	https://cmr.earthdata.nasa.gov/search/concepts/C3226082143-ENVIDAT.umm_json	Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dorée phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, <i>Scaphoideus titanus</i> and <i>Orientus ishidae</i>. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors.  The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both <i>S.titanus</i> and <i>O.ishidae</i>. Eventually, the need to adapt the current FD management strategies are highlighted.	proprietary
 gov.noaa.ncdc:C00842_Version 1.2	Blended 6-Hourly Sea Surface Wind Vectors and Wind Stress on a Global 0.25 Degree Grid (1987-2011)	NOAA_NCEI STAC Catalog	1987-07-09	2011-09-30	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2107093688-NOAA_NCEI.umm_json	The Blended Global Sea Surface Winds products contain ocean surface wind vectors and wind stress on a global 0.25 degree grid, in multiple time resolutions of 6-hourly and monthly, with an 11-year (1995-2005) monthly climatology. Daily files from a direct average of the 6-hourly data were also produced but are not included in this archive. The period of record is July 9, 1987 to September 30, 2011 for product Version 1.2, released in July 2007. Wind speeds were generated by blending available and selected microwave and scatterometer observations using a Simple spatiotemporally weighted Interpolation (SI) method. The following satellite retrieval datasets from Remote Sensing Systems (RSS) were used for Version 1.2: SSMI Version 6, TMI Version 4, QSCAT Version 3a, and AMSRE Version 5 (updated using the SSMI rain rate). The wind directions are from the NCEP-DOE Reanalysis 2 (NRA-2). The model wind directions are interpolated onto the blended wind speed grids. The 6-hourly satellite-scaled global 0.25-degree grid wind stresses are computed as: taux_s = -[(w_s/w_m)**2]*taux_m tauy_s = -[(w_s/w_m)**2]*tauy_m where 's' indicates satellite-scaled values and 'm' indicates NRA-2 model values interpolated to the satellite grid. Files are in netCDF format and available to users via FTP and THREDDS. A near real-time (NRT) variant of the product is generated quasi-daily to satisfy the needs of real-time users. The publicly available NRT data were replaced by the delayed-mode research quality data on a monthly basis through the end of September 2011, at which time the Seawinds production was impacted by the loss of data from the AMSR-E instrument failure. Production of the delayed-mode research products ends with the loss of AMSR-E in Version 1.2; a future version will extend beyond September 2011. The NRT products are continued after September 2011; however, this archive only includes the delayed-mode research products as the NRT data have a lower maturity rating removing the basis for archiving those data.	proprietary
 gov.noaa.ncdc:C01381_Not Applicable	AVHRR/HIRS Longwave Radiation Budget Data (RBUD)	NOAA_NCEI STAC Catalog	2000-03-18		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2107093896-NOAA_NCEI.umm_json	Radiation Budget Data - The Radiation Budget product suite is produced from the primary morning and afternoon Polar Orbiters. Product shows a measure of the longwave radiation emitted (W/m^2) by the earth-atmosphere system to space. The observations are displayed on a one degree equal area map for the day and night.  The products are: GAC long wave, HIRS long wave, longwave histogram, annual mean, monthly mean, and seasonal mean. This is a NESDIS legacy product and the file naming pattern is as follows: NPR.RBSD.[SatelliteID].D[YYDDD] or NPR.RBMD.[SatelliteID].D[YYDDD]	proprietary
 gov.noaa.ncdc:C01560_V3	Blended Global Biomass Burning Emissions Product - Extended (GBBEPx) from Multiple Satellites	NOAA_NCEI STAC Catalog	2018-01-09		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2107094570-NOAA_NCEI.umm_json	The Blended Global Biomass Burning Emissions Product version 3 (GBBEPx V3) system produces global biomass burning emissions. The product contains daily global biomass burning emissions (PM2.5, BC, CO, CO2, OC, and SO2) blended fire observations from MODIS Quick Fire Emission Dataset (QFED), VIIRS (NPP and JPSS-1) fire emissions, and Global Biomass Burning Emission Product from Geostationary satellites (GBBEP-Geo), which are in a grid cell of 0.25 × 0.3125 degree and 0.1 x 0.1 degree. It also produces hourly emissions from geostationary satellites, which is at individual fire pixels. The product output also include fire detection record in a HMS format, quality flag in biomass burning emissions, spatial pattern of PM2.5 emissions, and statistic PM2.5 information at continental scale. In Version3, daily biomass burning emissions at a FV3 C384 grid in binary format and daily biomass burning emissions at a 0.1 x 0.1 degree grid that include all the emissions species are added as new output.	proprietary
@@ -17815,10 +17390,7 @@ gpmxetc3vp_1	GPM Ground Validation Environment Canada (EC) Weather Station XET C
 gpmxpolifld_1	GPM GROUND VALIDATION IOWA X-BAND POLARIMETRIC MOBILE DOPPLER WEATHER RADARS IFLOODS V1	GHRC_DAAC STAC Catalog	2013-04-30	2013-06-16	-92.3511, 41.5293, -90.9114, 43.5375	https://cmr.earthdata.nasa.gov/search/concepts/C1983632696-GHRC_DAAC.umm_json	The GPM Ground Validation Iowa X-band Polarimetric Mobile Doppler Weather Radars IFloodS dataset was gathered during the IFloodS campaign from April to June 2013 throughout central and northeastern Iowa. The Iowa Flood Studies (IFloodS) was a ground measurement campaign that took place throughout Iowa from May 1 to June 15, 2013. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. Four X-band Polarimetric (XPOL) Mobile Doppler Weather Radars were used to collected high-resolution observations of precipitation. The data consists of reflectivity, Doppler velocity, spectrum width, differential reflectivity, differential phase, copolar correlation coefficient, and sound-to-noise ratios. These data are available in netCDF, and browse image files are available in .png format.	proprietary
 gppdi_npp_gridded_xdeg_1023_1	ISLSCP II Global Primary Production Data Initiative Gridded NPP Data	ORNL_CLOUD STAC Catalog	1970-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785304728-ORNL_CLOUD.umm_json	Net Primary Production (NPP) is an important component of the carbon cycle and, among the pools and fluxes that make up the cycle, it is one of the steps that are most accessible to field measurement. Direct measurement of NPP is not practical for large areas and so models are generally used to study the carbon cycle at a global scale.  This data set contains 2 *.zip files for above ground and total NPP data. 	proprietary
 gppdi_npp_point_1033_1	ISLSCP II GPPDI, Net Primary Productivity (NPP) Class B Point Data	ORNL_CLOUD STAC Catalog	1932-01-01	1999-12-31	156.7, 45.7, 176.6, 75.55	https://cmr.earthdata.nasa.gov/search/concepts/C2785314336-ORNL_CLOUD.umm_json	The Global Primary Production Data Initiative (GPPDI) was set up as a Focus 1 activity of the IGBP Data and Information System, a coordinated international program to improve worldwide estimates of terrestrial net primary productivity (NPP) for parameterization, calibration, and validation of NPP models at various scales.The GPPDI data collection contains documented field measurements of NPP for global terrestrial sites compiled from published literature and other extant data sources. The point measurements of NPP were categorized as either Class A, representing intensively studied or well-documented study sites (e.g., with site-specific climate, soils information, etc.), Class B, representing more numerous extensive sites with less documentation and site-specific information available, or Class C, representing regional collections of half-degree latitude-longitude grid cells. This data set in the ISLSCP II collection represents the GPPDI Class B NPP data. The Class B NPP data file contains biomass dynamics, climate, and site-characteristics data georeferenced to each site. There is one ASCII data file with this data set. 	proprietary
-gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0	GPS-derived data of SWE, HS and LWC and corresponding validation data	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.8093963, 46.8295131, 9.8093963, 46.8295131	https://cmr.earthdata.nasa.gov/search/concepts/C2789815057-ENVIDAT.umm_json	This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.    The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.    Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:   > Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431	proprietary
-grassland-use-intensity-maps-for-switzerland_1.0	Grassland-use intensity maps for Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082226-ENVIDAT.umm_json	A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.    The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.    We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).    The webcam-based reference data used for verification was subsequently added on 14.02.2024.	proprietary
 gravity_wilkes_1964_1	Gravity Survey Results, Wilkes Ice Cap, 1964-65	AU_AADC STAC Catalog	1964-01-01	1966-01-01	110, -68, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214308605-AU_AADC.umm_json	The results of a gravity survey done on Wilkes Ice Cap. No information in the papers on how it was done, dates, etc - just the numbers. Even year is unsure (could be 1964 or 1965 season).  These documents have been archived at the Australian Antarctic Division.	proprietary
-green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0	Green infrastructure in strategic spatial plans: Evidence from European urban regions	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-17.4023437, 33.5917433, 34.6289063, 68.4698482	https://cmr.earthdata.nasa.gov/search/concepts/C2789815116-ENVIDAT.umm_json	"The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.  The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.  The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article.  As a result, we provide the folowing outputs:  •	GI_Dataset_1_Items_1-12.xlsx – available on request  o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.   •	GI_Dataset_2_Items_12-36.csv – freely available   o	Results of the coding  on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper."	proprietary
 grinstedSBB-ECM-VIDEO_Not provided	2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen	SCIOPS STAC Catalog	1970-01-01		-11.042684, -74.57969, 11.11278, -74.566	https://cmr.earthdata.nasa.gov/search/concepts/C1214586809-SCIOPS.umm_json	Location: Scharffenbergbotnen blue ice area, Heimefrontfjella              Electrical Conductivity profile of the surface blue ice (stretching ~2.5km from near the ice fall). At the same time a video recording of the surface ice was made. Positions of the records can be tied together with DGPS.	proprietary
 gripapr2_1	GRIP AIRBORNE SECOND GENERATION PRECIPITATION RADAR (APR-2) V1	GHRC_DAAC STAC Catalog	2010-08-17	2010-09-22	-97.9192, 11.9008, -56.0457, 34.847	https://cmr.earthdata.nasa.gov/search/concepts/C1979833483-GHRC_DAAC.umm_json	The GRIP Airborne Second Generation Precipitation Radar (APR-2) dataset was collected from the Second Generation Airborne Precipitation Radar (APR-2), which is a dual-frequency (13 GHz and 35 GHz), Doppler, dual-polarization radar system. It has a downward looking antenna that performs cross track scans. Additional features include: simultaneous dual-frequency, matched beam operation at 13.4 and 35.6 GHz (same as GPM Dual-Frequency Precipitation Radar), simultaneous measurement of both like- and cross-polarized signals at both frequencies, Doppler operation, and real-time pulse compression (calibrated reflectivity data can be produced for large areas in the field during flight, if necessary). The APR-2 flew on the NASA DC-8 for the Genesis and Rapid Intensification Processes (GRIP) experiment and collected data between Aug 17, 2010 - Sep 22, 2010 and are in HDF-4 format. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes.	proprietary
 gripcaps_1	GRIP CLOUD MICROPHYSICS V1	GHRC_DAAC STAC Catalog	2010-08-13	2010-09-25	-100, 0, -71.5, 40	https://cmr.earthdata.nasa.gov/search/concepts/C1979834641-GHRC_DAAC.umm_json	The GRIP Cloud Microphysics dataset was collected during the GRIP campaign from three probes: the Cloud, Aerosol, and Precipitation Spectrometer (CAPS), the Precipitation Imaging Probe (PIP), and the Cloud Droplet Probe (CDP). All are manufactured by Droplet Measurement Technologies in Boulder, CO. The CAPS is a combination of two probes, the Cloud Imaging Probe-Greyscale (CIP-G), and the Cloud and Aerosol Spectrometer (CAS). Images of particles are recorded by the CIP-G and PIP, while the CAS probe measures particle size distribution from 0.55 to 52.5 microns and the CDP measures ice amount. Some ice/liquid water content are derived from the particle size distribution. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. Data was collected 13 Aug 2010 through 25 Sep 2010.	proprietary
@@ -17844,9 +17416,7 @@ gripnavgh_1	GRIP GLOBAL HAWK NAVIGATION AND HOUSEKEEPING DATA V1	GHRC_DAAC STAC
 gripnavwb57_1	GRIP WB-57 NAVIGATION AND HOUSEKEEPING DATA V1	GHRC_DAAC STAC Catalog	2010-08-27	2010-09-17	-96.3391, 17.2358, -71.5164, 30.6829	https://cmr.earthdata.nasa.gov/search/concepts/C1979859991-GHRC_DAAC.umm_json	The GRIP WB-57 Navigation and Housekeeping data was collected on flight days occuring between July 13 , 2010 to September 17, 2010 during the Genesis and Rapid Intensification Processes (GRIP) field campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. The NASA WB-57 is a weather research aircraft capable of operating for extended periods of time (~6.5 hours) from sea level to altitudes well over 60,000 feet (12 miles high). Both data in IWG1 format and error logs are part of this dataset.	proprietary
 gripradio_1	GRIP BARBADOS/CAPE VERDE RADIOSONDE V1	GHRC_DAAC STAC Catalog	2010-08-14	2010-09-24	-59.6251, 13.1, -24.8671, 16.8644	https://cmr.earthdata.nasa.gov/search/concepts/C1979860117-GHRC_DAAC.umm_json	The GRIP Barbados/Cape Verde radiosonde data set consists of generally two soundings per day (06Z and 12Z) launched from Barbados, and one sounding per day (12Z) launched from Cape Verde during the Genesis and Rapid Intensification Processes (GRIP) field campaign. The major goal was to better understand how tropical storms form and develop into major hurricanes. These radiosondes measure the profile of atmospheric pressure, temperature, humidity, wind speed and direction, from the ground to an altitude of up to 40 km (in general, the sondes reached at least a pressure of 100 milibars). The launch program began on August 14, 2010 and ended September 24, 2010. The sondes used were type DFM-06, built by GRAW Radiosondes, Nuremberg Germany. Most ascents were done with TOTEX 200-g latex balloons using the DMF-06 sondes. A few launches were made using TOTEX 800-g Balloons with the DFM-97 package (connected with ECC ozonesonde). On some days launch times were changed, and multiple launches were made from Barbados on September 9, 10 and 21. The data were retrieved using a GRAWMET GS-E ground station. The sample rate of the data was 4 seconds for the Barbados data and 2 seconds for the Cape Verde data.	proprietary
 gripstorm_1	GRIP Hurricane and Tropical Storm Forecasts V1	GHRC_DAAC STAC Catalog	2010-08-12	2010-11-14	-178.5, 0.8, 0, 87.6	https://cmr.earthdata.nasa.gov/search/concepts/C1979860341-GHRC_DAAC.umm_json	The GRIP Hurricane and Tropical Storm Forecasts dataset consists of tropical cyclone model forecast tracks archived during the NASA Genesis and Rapid Intensification Processes (GRIP) field campaign. GRIP was one of three hurricane field campaigns conducted during the 2010 Atlantic/Pacific hurricane season. This tri-agency effort included NASA GRIP, the NSF Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) and the NOAA Intensity Forecasting Experiment 2010 (IFEX10). The hurricane and tropical storm forecasts data files are available from August 12 through November 14, 2010 in ASCII text format with browse files in KML format, viewable in Google Earth.  The ASCII text files contain 5-day model “consensus” forecasts and the KML browse files contain model forecasts ranging from 5-days to 10-days.	proprietary
-groundwater-time-series-studibach-rinderer-et-al-2019-wrr_1.0	Groundwater time series Studibach (Rinderer et al., 2019, WRR)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.7220859, 47.0382382, 8.7220859, 47.0382382	https://cmr.earthdata.nasa.gov/search/concepts/C2789815127-ENVIDAT.umm_json	Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R	proprietary
 gtopo30_hydro_1k_Not provided	GTOPO30 Hydro 1K	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567910-USGS_LTA.umm_json	HYDRO1k is a geographic database developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale.	proprietary
-gtree_1.0	G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.86624, 46.771906, 9.86624, 46.771906	https://cmr.earthdata.nasa.gov/search/concepts/C2789815146-ENVIDAT.umm_json	# Background information  Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.     # Experimental design  The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).     # Data description  All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).	proprietary
 gts_precip_daily_xdeg_1001_1	ISLSCP II Gauge-Based Analyses of Daily Precipitation over Global Land Areas	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784899029-ORNL_CLOUD.umm_json	The objective of this work was to construct a long-term data set of daily precipitation on half degree and one degree latitude/longitude grids over the global land areas. The analyses are defined by interpolating station observations from GTS (Global Telecommunications System) gauges using the algorithm of Shepard (1968). The algorithm of Shepard (1968) has been widely used to interpolate gauge observations of monthly, pentad, and daily precipitation (Rudolf 1993, Xie et al. 1996). This algorithm is used to interpolate the irregularly distributed station observations onto grid points. The weighting coefficients are inversely proportional to the gauge-grid point distance and are adjusted by a cosine function taking into account the directional isolation of each gauge relative to all other nearby gauges. There are 6 data files with this data set.	proprietary
 h01_shd_253_1	BOREAS HYD-01 Soil Hydraulic Properties	ORNL_CLOUD STAC Catalog	1994-01-01	1994-12-31	-106.2, 53.63, -98.29, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2812440659-ORNL_CLOUD.umm_json	Contains the hydraulic properties of the soil at each tower flux site determined by the HYD-01 science team.	proprietary
 h01smpvd_255_1	BOREAS HYD-01 Volumetric Soil Moisture Data	ORNL_CLOUD STAC Catalog	1994-05-25	1997-06-26	-105.32, 53.66, -98.29, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2807624113-ORNL_CLOUD.umm_json	Contains the percent soil moisture by volume data that was collected by the HYD-01 group at the various tower sites.	proprietary
@@ -17881,7 +17451,6 @@ h9rgbl96_233_1	BOREAS 1996 HYD-09 Belfort Rain Gauge Data	ORNL_CLOUD STAC Catalo
 h9rgtb94_230_1	BOREAS 1994 HYD-09 Tipping Bucket Rain Data	ORNL_CLOUD STAC Catalog	1994-04-10	1994-12-31	-105.14, 53.9, -98.34, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2807609831-ORNL_CLOUD.umm_json	Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.	proprietary
 h9rgtb95_232_1	BOREAS 1995 HYD-09 Tipping Bucket Rain Data	ORNL_CLOUD STAC Catalog	1995-01-01	1995-12-31	-105.14, 53.9, -98.34, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2807610223-ORNL_CLOUD.umm_json	Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.	proprietary
 h9rgtb96_234_1	BOREAS 1996 HYD-09 Tipping Bucket Rain Data	ORNL_CLOUD STAC Catalog	1996-01-01	1996-12-31	-105.14, 53.9, -98.34, 55.94	https://cmr.earthdata.nasa.gov/search/concepts/C2807611306-ORNL_CLOUD.umm_json	Contains the Tipping Bucket rain gauge data that was collected by the HYD09 group at various locations.	proprietary
-habitat-map-of-switzerland_1.0	The Habitat Map of Switzerland v1	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815160-ENVIDAT.umm_json	Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse The FOEN funded project ‘Developing a Habitat Map of Switzerland’ conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only.  Habitats are mapped through a variety of approaches that can be grouped as either: 1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.	proprietary
 hamsrcpex_1	High Altitude MMIC Sounding Radiometer (HAMSR) CPEX V1	GHRC_DAAC STAC Catalog	2017-05-24	2017-07-16	-99.95, 5.05, -45.05, 39.95	https://cmr.earthdata.nasa.gov/search/concepts/C2624567100-GHRC_DAAC.umm_json	The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. Data are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.	proprietary
 hamsrcpexaw_1	High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-AW V1	GHRC_DAAC STAC Catalog	2021-08-17	2021-09-04	-118.078, 11.768, -45.122, 34.613	https://cmr.earthdata.nasa.gov/search/concepts/C2257989308-GHRC_DAAC.umm_json	The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-AW dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. HAMSR is mounted in payload zone 3 near the nose of the Global Hawk NASA aircraft. Data is available from August 17, 2021 through September 4, 2021 in netCDF-3 format, with associated browse files in PNG format.	proprietary
 hamsrcpexcv_1	High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-CV	GHRC_DAAC STAC Catalog	2022-09-06	2022-09-30	-40.6360016, 1.848, 3.9360001, 79.5830002	https://cmr.earthdata.nasa.gov/search/concepts/C2704126285-GHRC_DAAC.umm_json	The High Altitude MMIC Sounding Radiometer (HAMSR) CPEX-CV dataset includes measurements gathered by the HAMSR instrument during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region.  HAMSR has 25 spectral channels which are split into 3 bands to provide measurements that can be used to infer the 3-dimensional distribution of temperature, water vapor, and cloud liquid water profiles in the atmosphere, even in the presence of clouds. Data are available from September 6-30, 2022  in netCDF-4 format.	proprietary
@@ -17906,19 +17475,14 @@ heard_refuge0304_gis_1	Heard Island Field Camp and Refuge Locations 2003/04	AU_A
 heard_satimage_control_1	Heard Island - Ground Control Point Identification on 4 satellite images	AU_AADC STAC Catalog	2003-01-17	2005-02-09	73.23, -53.05, 73.41, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214313540-AU_AADC.umm_json	"Ground control points were captured in the field as described in the metadata record ""Global change, biodiversity and conservation in terrestrial and coastal ecosystems on Heard and McDonald Islands [ASAC_1181]"".  The aim of this project was to identify the control points on various satellite images (AADC Image IDs 253, 267, 446 and 447) to determine image coordinates for the control points. These would then allow the satellite image to be adjusted to the ground control.  There is a shapefile for each image with the features in the shapefile moved to the corresponding image location.  Attributes in the shapefile include image coordinates (UTM43) and control point coordinates."	proprietary
 heard_species_checklist_1	Heard Island Species Checklist	AU_AADC STAC Catalog	2002-04-12	2002-04-12	73.24, -53.21, 73.9, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214308628-AU_AADC.umm_json	A checklist of species that have been recorded or observed at Heard Island during various historical and ANARE scientific expeditions to the island.  The data are held in the Australian Antarctic Data Centre Biodiversity Database, and updated as required.	proprietary
 heard_vegetation_gis_1	Heard Island Vegetation GIS Dataset	AU_AADC STAC Catalog	1988-01-09	2000-12-01	73.24, -53.21, 73.9, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214308644-AU_AADC.umm_json	Heard Island and McDonald Islands, vegetation layer. This is a polygon dataset stored in the Geographical Information System (GIS). The data represents approximately the areas of vegetation cover on these islands.	proprietary
-heavy-metals-and-acid-rain-effects-on-aphids-and-caterpillars_1.0	Plant-mediated effects of heavy metals and acid rain on feeding aphids and caterpillars	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.4562111, 47.3623623, 8.4562111, 47.3623623	https://cmr.earthdata.nasa.gov/search/concepts/C2789815177-ENVIDAT.umm_json	In controlled model forest ecosystems young trees were exposed to heavy metals in the soil and to acid precipitation. On spruce trees Lymantria monacha caterpillars and Cinara pilicornis aphids and on willow Pterocomma pilosum aphids were reared and monitored. Developmental time and fecundity of L. monacha were recorded and in aphids colony growth was measured.	proprietary
 heliemps_1	Effects of helicopter operations on emperor penguin chicks	AU_AADC STAC Catalog	1997-11-21	1997-11-21	45, -90, 160, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214313541-AU_AADC.umm_json	Creching emperor penguin (Aptenodytes forsteri) chickswere exposed to two overflights by an S-76 twin engine helicopter at 1000 m: a current operational guideline for helicopter activity in Antarctica. The flights were conducted on the same day but under different wind conditions: a morning flight with a 10 kt (18 km.hr-1) katabatic blowing perpendicular to the direction of helicopter travel and an afternoon flight with virtually no wind. Background noise levels recorded in the morning, before the helicopter flight, were significantly higher than in the afternoon, but these differences were not detectable when the helicopter was overhead. There were also no significant differences in the way chicks responded to helicopters between the morning and afternoon flight. All chicks became more vigilant when the helicopter approached and 69% either walked or ran, generally moving less than 10 m toward other chicks (i.e. not scattering). Most chicks (83%) displayed flipper-flapping, probably indicating nervous apprehension. This behaviour was seldom displayed in the absence of disturbance.  Although all effects were relatively transitory, results support the introduction of more conservative guidelines for helicopter operations around breeding localities of this species.  The fields in this dataset are:  Time Action Date Lying Standing Walking Preening Flapping	proprietary
 helimaps_3	Maps for helicopter operations in the Australian Antarctic Territory	AU_AADC STAC Catalog	1999-07-03	2011-05-31	45, -90, 160, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214308647-AU_AADC.umm_json	"A series of maps were produced for the publication ""Flight paths for helicopter operations in the Australian Antarctic Territory"", originally published in hard copy in September 2000. These superseded a series published in 1999.  A new edition of the maps was produced in 2011.  The maps are digitally available from the SCAR Map Catalogue. See the Related URL below."	proprietary
 helipenguins_1	Effects of helicopters on Antarctic wildlife	AU_AADC STAC Catalog	1995-10-10	1998-02-12	45, -90, 160, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214313522-AU_AADC.umm_json	This study aimed to quantify the effects of helicopter operations on Antarctic wildlife, with an emphasis on determining minimum safe over-flight altitudes and landing distances for a range of species.  An experimental approach was adopted whereby wildlife were exposed to helicopters either over-flying or landing at specific altitudes or distances while the behaviour, and in some cases physiology, of individual animals were recorded. Two types of helicopters were used in the study: a Sikorsky S-76 (twin engine) and a Squirrel AS350 (single engine). This metadata record relates to the responses of Adelie Penguins (Pygoscelis adeliae) over a number of phases of their breeding cycle.  The fields in this dataset are:  Time Action Date	proprietary
-herb-layer-biomass-in-swiss-forests_1.0	Herb layer biomass in Swiss forests	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	7.195934, 46.0807887, 9.1971004, 47.5101827	https://cmr.earthdata.nasa.gov/search/concepts/C2789815199-ENVIDAT.umm_json	The purpose of this project was to develop a model to estimate herb layer biomass and carbon stock based on the categorical cover estimate on each NFI sample plot. To this end, biomass and cover of the six main plant groups in the herb layer were collected from 405 1x1 m subplots on 135 study sites (15 sites in 9 strata) which were selected based on a stratified sampling approach. To ensure consistency with NFI methodology, study sites corresponded to the design of regular NFI sample plots and plant cover was estimated by trained field-crew members. Based on the dry weight of the plant biomass and the cover estimate on each subplot, a linear regression model was developed and applied to estimate herb layer biomass on each NFI sample plot.	proprietary
-high-resolution-static-data-for-wrf-over-switzerland_1.0	High resolution static data for WRF over Switzerland	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	4, 45, 12, 49	https://cmr.earthdata.nasa.gov/search/concepts/C2789815211-ENVIDAT.umm_json	Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.	proprietary
 highjump_scans_1	Digital images of Operation Highjump aerial photography	AU_AADC STAC Catalog	1947-01-01	1948-12-31	45, -90, 160, -60	https://cmr.earthdata.nasa.gov/search/concepts/C1214311059-AU_AADC.umm_json	The US Navy during Operation Highjump carried out the earliest comprehensive acquisitions in 1947-48. This operation included an intensive program of trimetrigon aerial photography acquisitions of the whole of the coastline of Antarctica and some inland areas. 50 CDs worth of images have been scanned. The Australian Antarctic Data Centre's holdings of Operation Highjump aerial  photography can be searched using the Data Centre's online Aerial Photograph Catalogue (see link below). On the search page the Operation Highjump aerial  photography can be selected as an Aerial Photography Series.	proprietary
 highres_10be_records_law_dome_1999_2009_2	High Resolution 10Be Records, spanning 1999-2009 from Law Dome, Antarctica	AU_AADC STAC Catalog	1999-01-01	2009-12-31	112.7, -66.78, 112.9, -66.76	https://cmr.earthdata.nasa.gov/search/concepts/C1214313526-AU_AADC.umm_json	This file comprises five high-resolution records of 10Be concentration in snow from Law Dome, East Antarctica: DSS0102-pit, DSS0506-pit, DSS0506-core, DSS0809-core and DSS0910-core.  A single composite series is constructed from three of these records (DSS0506-core, DSS0809-core and DSS0102-pit), providing a monthly-resolved time-series of 10Be concentrations at DSS over the decade spanning 1999 to 2009.  This work was done as part of AAS 2384, AAS 3064 and AAS 1172.  A data update was provided by Jason Anderson on 2012-12-17.	proprietary
-hillshade-for-vegetation-height-model-nfi_2016 (current)	Hillshade for Vegetation Height Model NFI	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815239-ENVIDAT.umm_json	Hillshade of the digital surface model (DSM), calculated from digital aerial stereo images. The image data was acquired by the Federal Office of Topography swisstopo. The resolution of the DSM is 1 m x 1 m.	proprietary
 historic_cropland_xdeg_966_1	ISLSCP II Historical Croplands Cover, 1700-1992	ORNL_CLOUD STAC Catalog	1700-01-01	1992-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784888334-ORNL_CLOUD.umm_json	The Historical Croplands Cover data set was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, this data set can be used to study how global changes in cultivated area has influenced climate, biogeochemical cycles, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models.This is a gridded data set describing the fraction of each grid cell in the globe that is occupied by cultivated land from 1700 to 1992. Data layers are provided for every 50 years from 1700 to 1850, every 10 years from 1850 to 1980, and every year from 1986 to 1992.There are two sources of global land cover/land use data. The most recent estimates are derived from satellite measurements, and are available in a spatially-explicit fashion for roughly the last 30 years. The other estimate is based on ground-based sources such as census statistics, land surveys, estimates by historical geographers, etc. These land inventory data are only available at the scale of political units, but have the advantage of being historical. Ramankutty and Foley (1998) derived a spatially-explicit data set of croplands in 1992 by synthesizing remotely-sensed land cover data with contemporary land inventory data. Furthermore, Ramankutty and Foley (1999) extended this data set into the past (back to 1700) using historical land inventory data.The data set should only be used for continental-to-global scale analysis and modeling. The data set captures the broad patterns of cropland change over history, but not necessarily the fine details at local to regional scales - please check the data quality before using it at fine spatial scales. The quality of historical data for the Russian Federation is poor. The quality of data prior to 1850 is poor -- only continental-scale historical data were used for that period. 	proprietary
 historic_landcover_xdeg_967_1	ISLSCP II Historical Land Cover and Land Use, 1700-1990	ORNL_CLOUD STAC Catalog	1700-01-01	1990-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784888644-ORNL_CLOUD.umm_json	The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).Testing against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period. 	proprietary
 historical_croplands_675_1	LBA Regional Historical Croplands, 5-min, 1900-1992 (Ramankutty and Foley)	ORNL_CLOUD STAC Catalog	1900-01-01	1992-12-31	-80, -18, -35, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2777324668-ORNL_CLOUD.umm_json	This data set is a subset of a global croplands data set (Ramankutty and Foley 1999a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format at 5-min resolution.Navin Ramankutty and Jonathan Foley, of the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, developed a global, spatially explicit data set of reconstructed historical croplands from 1700 to 1992. The method for historical reconstruction used a simple algorithm that linked contemporary satellite data and historical cropland inventory data. A spatially explicit croplands data set for 1992 was first derived by calibrating a satellite-derived land cover classification data set against cropland inventory data for 1992. This derived data set was then used within a simple land cover change model, along with historical cropland inventory data, to derive spatially explicit maps of historical croplands. The global data set was restricted to a representation of permanent croplands (i.e., excluding shifting cultivation), which follows the Food and Agriculture Organization (FAO) definition of arable lands and permanent crops. Data values represent fraction of grid cell in croplands.Data for the LBA study area are available for the years 1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990, and 1992. Although the global croplands data set contains data representing croplands since 1700, essentially no croplands were in the LBA study area until 1900. Data from previous years were excluded at the suggestion of the data originator.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/historical_croplands/comp/uwcrop_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.	proprietary
-history-of-wetlands-in-switzerland-since-1850_1.0	History of wetlands in Switzerland since 1850	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815260-ENVIDAT.umm_json	"Naturally, large parts of the Swiss Plateau are characterised by wetlands  and meandering rivers. That this is no longer the case today is the result  of centuries of efforts to obtain dry land. But how did this process take  place? What were the relevant actors and what were their motivations? And  what can be said about the ecological consequences of this development?    In a research project on the history of wetlands in Switzerland since 1700,  we conducted (a) a historical analysis of the development of land use in  wetlands and the actors involved, (b) a historical-cartographic  reconstruction of wetland extent since 1850 and (c) an evaluation of  ecological effects of changes in wetlands on various organisms groups.    The series of GIS layers on wetland history stem from the second part of  the project. The area reconstruction is based on digitized and homogenized  signatures from national map series, as they have been available since  about 1850. Details about the digitalization process and the homogenization  procedures applied (""Rekonstruktionen"") are included in Stuber & Bürgi  2019.    __Book Citation:__   > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. ""Bristol Schriftenreihe"", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten."	proprietary
 hiwat_1	High-Impact Weather Assessment Toolkit (HIWAT)	GHRC_DAAC STAC Catalog	2017-04-02		60.562, 10.632, 111.438, 45.951	https://cmr.earthdata.nasa.gov/search/concepts/C2756158683-GHRC_DAAC.umm_json	The High Impact Weather Assessment Toolkit (HIWAT) uses a mesoscale numerical weather prediction model and the Global Precipitation Measurement (GPM) constellation of satellites. The toolkit includes a suite of ensemble model forecasts to constrain uncertainties and provide a probabilistic forecast for improved decision-making. The toolkit provides outlooks for lightning strikes, high-impact winds, high rainfall rates, hail damage, and other weather events. The toolkit provides a 54-hour probabilistic forecast over Nepal and Bangladesh along with parts of northeast India (i.e., the Hindu Kush Himalayan region). HIWAT will also support threat assessments, such as thunderstorm intensity, using GPM and impact assessments using Landsat/MODIS land imagery to identify damage scars. The dataset files are available from April 2, 2017, through October 2, 2022, in netCDF-3 format.	proprietary
 hiwrapimpacts_1	High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-25	2023-02-28	-95.46, 31.073, -64.894, 48.658	https://cmr.earthdata.nasa.gov/search/concepts/C1995871767-GHRC_DAAC.umm_json	The High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) IMPACTS dataset consists of Equivalent reflectivity factor, Doppler velocity, Doppler velocity spectrum width, Linear Depolarization Ratio (LDR), Ocean normalized radar cross-section, Co-polarization signal-to-noise mask estimates collected by the HIWRAP onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. These data are available from January 25, 2020, through February 28, 2023, in HDF-5 format.	proprietary
 holme_bay_1_10000_gibbney_1	Holme Bay 1:10000 Gibbney Island Mapping	AU_AADC STAC Catalog	1998-12-08	2001-09-05	61.58, -68.17, 64.07, -67.37	https://cmr.earthdata.nasa.gov/search/concepts/C1214311142-AU_AADC.umm_json	This is the metadata record for the Holme Bay 1:10000 Gibbney Island Mapping (DQI 332) mapped in March 2001 by Hydro Tasmania.	proprietary
@@ -17928,7 +17492,6 @@ holme_bay_1_5000_B-W_Islands_1	Holme Bay 1:5000 Bechervaise and Welch Islands Ma
 holme_bay_dem_1	Digital Elevation Model of Holme Bay, Antarctica	AU_AADC STAC Catalog	1996-03-18	2003-05-01	61.9782, -67.6654, 63.1963, -67.3891	https://cmr.earthdata.nasa.gov/search/concepts/C1611351038-AU_AADC.umm_json	A Digital Elevation Model (DEM) of the continent coast and islands of Holme Bay, Antarctica with cell size 10 metres was interpolated from input coastline, contour and spot height (point locations with an elevation attribute) data.   The input data was sourced from the following datasets which are listed with their dataset number: Framnes Mountains 1:25000 Topographic GIS Dataset (dataset 55) Holme Bay 1:25000 GIS Dataset (dataset 57) Holme Bay 1:10000 Gibbney Island Mapping (dataset 90)  Holme Bay 1:10000 Rookery Islands Mapping (dataset 91) Holme Bay 1:10000 Wigg Island Mapping (dataset 96) Holme Bay 1:5000 Bechervaise and Welch Islands Mapping (dataset 97) Mapping around the Framnes Mountains from Spot Imagery at 10 metre pixel resolution (dataset 98) Robinson Group, east of Mawson, mapping from Spot satellite imagery at 20 metre pixel resolution (dataset 99) Framnes Mountains contours smoothed and edited (dataset 187).   The spatial coverages and estimated planimetric and vertical accuracies of datasets 57, 90, 91, 96, 97 and 99 are shown in a map linked to this metadata record. These datasets were the source data for the islands and a strip of the continent coast which was the area of interest when the DEM was created. The source data for almost all of the remaining land area, which is inland from a coastal strip, was contours from dataset 187 with an estimated planimetric accuracy of 200 metres and estimated vertical accuracy of 100 metres.  The interpolation was done using the Topo to Raster tool in ArcGIS. The output DEM was clipped to the extents of the input data. The dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool. The DEM is stored in the UTM Zone 41S projection. The horizontal datum is WGS84. The vertical datum is Mean Sea Level. The DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade. The DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record: 1 A geotiff; and  2 An ascii file in ESRI's ascii format for rasters.	proprietary
 holme_bay_gis_1	Holme Bay 1:25,000 gis dataset	AU_AADC STAC Catalog	1998-12-08	2001-09-05	61.58, -68.17, 64.07, -67.37	https://cmr.earthdata.nasa.gov/search/concepts/C1214313549-AU_AADC.umm_json	The Holme Bay geographical infromation system (GIS) dataset includes the following features: spot heights, geology (erratics only) contours with a 5m contour interval, ice, crevasse fields, hillocks, melt lakes, moraines, rock, snow, cliffs, drift tails, flowlines, glaciers, grounding lines, ridge lines, streams, penguins, masts, lakes and refuges. The data ranges from Low Tongue to Paterson Islands along the Mawson Coast.  Data was captured in March 2001, photogrammetically from aerial photography. The aerial photography was captured in March - April 1996 from films ANTC1024, ANTC1025, ANTC1026, ANTC1029, ANTC1031, ANTC1032, ANTC1034	proprietary
 holme_penguin_gis_1	Holme Bay penguin colonies digitised from 1992-3 Linhof aerial photography	AU_AADC STAC Catalog	1992-12-09	1993-01-01	62.61, -67.67, 62.79, -67.53	https://cmr.earthdata.nasa.gov/search/concepts/C1214311122-AU_AADC.umm_json	Aerial photography (Linhof) of penguin colonies was acquired over the Holme Bay (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Data conforms to SCAR Feature Catalogue which can be searched (refer to link below).	proprietary
-how-do-stability-corrections-perform-in-the-stable-boundary-layer-over-snow_1.0	How do stability corrections perform in the stable boundary layer over snow?	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815275-ENVIDAT.umm_json	We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.  The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.   Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.  The selected datasets and corresponding test sites are briefly introduced below:    Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).    Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.     Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).      Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).    Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).	proprietary
 hs3avaps2_2	HURRICANE AND SEVERE STORM SENTINEL (HS3) GLOBAL HAWK ADVANCED VERTICAL ATMOSPHERIC PROFILING SYSTEM (AVAPS) DROPSONDE SYSTEM V2	GHRC_DAAC STAC Catalog	2012-09-07	2014-09-30	-97.5988, 7.9001, -19.0779, 47.1646	https://cmr.earthdata.nasa.gov/search/concepts/C1979860836-GHRC_DAAC.umm_json	The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Advanced Vertical Atmospheric Profiling System (AVAPS) Dropsonde System dataset was collected by the Advanced Vertical Atmospheric Profiling System (AVAPS), built by the National Center for Atmospheric Research (NCAR), which served as the dropsonde system for the Global Hawk aircraft during the HS3 campaign. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. AVAPS dropsondes provide in-situ, high-vertical resolution measurements of atmospheric variables including pressure, temperature, humidity, geographic location, and winds, providing a vertical profile of the atmospheric conditions. The raw instrument measurement precision is as follows: pressure +-1.0 hPa, temperature +-0.2 degrees C, wind +-1 ms-1, and humidity +-7 percent. The measured information was transmitted via Iridium or Ku-Band satellite to the ground station where the Global Telecommunications System (GTS) performed additional processing for research and operational purposes.	proprietary
 hs3cimssbt_1	Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Brightness Temperature V1	GHRC_DAAC STAC Catalog	2014-08-14	2014-10-03	-180, 12, -60, 52	https://cmr.earthdata.nasa.gov/search/concepts/C1983208537-GHRC_DAAC.umm_json	The Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Brightness Temperature dataset contains infrared images of brightness temperature from the 15th Geostationary Operational Environmental Satellite (GOES-15) and the 10th Meteorological Satellite (METEOSAT-10) during the Hurricane and Severe Storm sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The images are available for dates between August 14, 2014 and October 3, 2014 at 15 minutes intervals in PNG format.	proprietary
 hs3cimsscth_1	Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Cloud Top Height V1	GHRC_DAAC STAC Catalog	2014-08-14	2014-10-03	-180, 12, -60, 52	https://cmr.earthdata.nasa.gov/search/concepts/C1983220658-GHRC_DAAC.umm_json	The Hurricane and Severe Storm Sentinel (HS3) Cooperative Institute for Meteorological Satellite Studies (CIMSS) Cloud Top Height dataset contains could top height images obtained from infrared observations of the 15th Geostationary Operational Environmental Satellite (GOES-15) and the 10th Meteorological Satellite (METEOSAT-10) using the Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA) for the Hurricane and Severe Storm sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The images are available for dates between August 14, 2014 and October 3, 2014 at 15 minutes intervals in PNG format.	proprietary
@@ -17945,57 +17508,29 @@ hs3ships_1	Hurricane and Severe Storm Sentinel (HS3) Statistical Hurricane Inten
 hs3shis_1	Hurricane and Severe Storm Sentinel (HS3) Scanning High-Resolution Interferometer Sounder (S-HIS) V1	GHRC_DAAC STAC Catalog	2011-09-01	2014-09-30	-154.611, 7.55205, -19.2257, 50.0961	https://cmr.earthdata.nasa.gov/search/concepts/C1979871558-GHRC_DAAC.umm_json	The Hurricane and Severe Storm Sentinel (HS3) Scanning High-Resolution Interferometer Sounder (S-HIS) measures emitted thermal radiances that are used to obtain temperature and water vapor profiles of the Earth's atmosphere in clear-sky conditions.  Due to the S-HIS scanning capability, the instrument provides 2 km resolution (at nadir) across a 40 km wide ground swath when flown at an altitude of 20 km onboard the NASA Global Hawk unmanned aircraft. S-HIS data were collected during the 5-week HS3 field campaign study periods in the 2012 to 2014 Atlantic hurricane seasons.  	proprietary
 hs3wwlln_1	HURRICANE AND SEVERE STORM SENTINEL (HS3) WORLD WIDE LIGHTNING LOCATION NETWORK (WWLLN) STORMS V1	GHRC_DAAC STAC Catalog	2012-08-28	2014-10-20	-116.595, 12.9, -15.001, 68.994	https://cmr.earthdata.nasa.gov/search/concepts/C1979872496-GHRC_DAAC.umm_json	The World Wide Lightning Location Network (WWLLN) is a global, ground-based lightning sensor network operated by the University of Washington in Seattle.  This network monitors and maps global lightning activity.  WWLLN has generated quality controlled global lightning data for storms studied during the 2012-2014 Hurricane and Severe Storm Sentinel (HS3) campaign to track and analyze lightning activity.	proprietary
 husky_sat_1	Husky Massif Satellite Image Map 1:100000	AU_AADC STAC Catalog	1992-07-01	1992-07-31	64.83, -72.23, 68.29, -70.77	https://cmr.earthdata.nasa.gov/search/concepts/C1214311161-AU_AADC.umm_json	Satellite image map of Husky Massif, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1992.  The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 131-110, 129-110, 129-111). It is projected on a Transverse Mercator projection, and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
-hydraulic-resistance-of-pores-in-porous-media-using-dns-of-laminar-flow_1.0	Hydraulic resistance of pores in porous media using DNS of laminar flow	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.5274506, 47.3806378, 8.5274506, 47.3806378	https://cmr.earthdata.nasa.gov/search/concepts/C2789815300-ENVIDAT.umm_json	"Included are three direct numerical simulations results of Stokes flow in three heterogeneous porous media obtained with OpenFoam simulations. In addition we include three data files that contain point-based extracted pores based on the post-processing as reported in the submitted paper ""Local hydraulic resistance in heterogeneous porous media"" in GRL."	proprietary
-hydro-ch2018-evolution-of-stream-and-lake-water-temperature-under-climate-change_1.0	Hydro-CH2018 Evolution of stream and lake water temperature under climate change	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815311-ENVIDAT.umm_json	This report presents past observations and projects the future development of water temperature in Swiss lakes and rivers. Projections are made until the end of the 21st century using the CH2018 climate scenarios. Besides climate change effects on temperature, we also discuss effects on discharge for rivers, and effects on the thermal structure, and specifically the seasonal mixing regime and ice cover of lakes.	proprietary
-hydro-ch2018-reservoirs_1.0	Hydro-CH2018 reservoirs	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815063-ENVIDAT.umm_json	"The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.   Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.    Water demand was estimated for six categories:     - 1) Drinking water (households and tourism),  - 2) industry (second and third sector),   - 3) artificial snow production,   - 4) agriculture (irrigation and livestock feeding),   - 5) ecology (residual flows), and   - 6) hydropower.    Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.  Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.    A detailed description of the simulation and estimation procedures can be found in     *   Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.    This dataset contains the following information:    1.	Shapefile with the 307 medium-sized Swiss catchments.  2.     Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.  3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.  4.	Textfiles with the storage volumes per category and catchment."	proprietary
-hydro-ch2018-snow_1.0	Snow and the water cycle in a changing climate	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815112-ENVIDAT.umm_json	This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff  currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.  Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.   As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.	proprietary
-hydro-meteorological-simulations-1981-2018_1.0	Hydro-meteorological simulations for the period 1981-2018 for Switzerland	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815122-ENVIDAT.umm_json	The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.	proprietary
 hydro1k_elevation_xdeg_1007_1	ISLSCP II HYDRO1k Elevation-derived Products	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785285265-ORNL_CLOUD.umm_json	This data set contains coarse scale elevation and elevation-based parameters at 1.0 and 0.5-degree spatial resolutions that were developed to support a wide variety of global modeling activities through the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection. These coarse scale data have sufficient statistical information (up to fourth moment) to allow a good statistical description of the sub-cell distribution of any particular elevation parameter (i.e. elevation, slope and aspect). The database used in the development effort was the HYDRO1k product (http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/HYDRO1K) with a native spatial resolution of 1 km, the highest resolution database of global coverage of standard elevation-based derivatives (slope, aspect, elevation, compound topographic index, etc.). 	proprietary
 hydrographic_gghydro_636_1	SAFARI 2000 Hydrographic Data, 1-Deg, Release 2.2 (Cogley)	ORNL_CLOUD STAC Catalog	1970-01-01	1990-01-01	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2797365245-ORNL_CLOUD.umm_json	This southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1-degree longitude by 1-degree latitude. The data are provided in both ASCII GRID and binary image file formats.	proprietary
-hydropot_integral_1.0	HYDROpot_integral	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815135-ENVIDAT.umm_json	## A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)    ## Introduction  The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.  To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.    ## Material and method  The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.  A list of data sources can be found here [link to text file]  A list of all parameters can be downloaded here [link to PDF and text files]    ## Dataset description  Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.     ## Changes in the methodology compared to the original method (Hirschi et. al 2013)     * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;  individual cantons such as Zug and St. Gallen have no mapped values according to the modular  concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not  completely (BAFU 2009).   * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.  centrally available, therefore values in GR were equated with NA.   * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values  equal to NA.   * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under  Federal control and dams serving hydroelectricity (Dam, as of 2013).   * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,  Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.  is affected.   * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.  A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This  evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).  Therefore, an evaluation of the station data is made where critical and possible river segments  with poor quality (median less than 3) exist. Only two longer and one short  sections in Switzerland receive a lower median than 3 for water quality.   * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of  Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number  of sections, a large number of viewpoints in the layer of Swisstopo and  the computation time and computability in ArcGIS, the landscape area is classified as generally viewable  (equal to 1).  16 Method  Additional indicators were added (see Appendix B.2):   * SLS_A21 Dissection   * SLS_A22 Forest areas   * SLS_B03 Hiking trails   * SLS_B10 Residential and vacation homes   * SLS_B11 Tourist infrastructure   * SLS_C01 Landfill   * SLS_C03 Infrastructure   * SLS_C05 Industry   * SLS_C06 Agricultural land  Not to be added, although present to some extent:   * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is  for the whole of Switzerland is limited   * SLS_A15 Legally binding protection and land use planning: the individual river sections are not  clearly designated, i.e. no geodata exist  The following data are also not supplemented, as they are cantonal data:   * SLS_A10 Cantonal nature reserves   * SLS_A16 Forest reserves   * SLS_A17 Cantonal inventories and contractually protected areas	proprietary
-hymenoptera_1.0	Hymenoptera	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815153-ENVIDAT.umm_json	Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.    Data are provided on request to contact person against bilateral agreement.	proprietary
 iagp_casey_traverse_results_1	Data Report on 1981 Traverses From Casey For IAGP	AU_AADC STAC Catalog	1981-01-01	1981-12-31	110, -67, 111, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311123-AU_AADC.umm_json	The Casey Traverse Program was Australia's major contribution to the International Antarctic Glaciological Project, aimed at determining the galciological regime and processes, and deducting some of the history and future of a sizeable part of the east Antarctic ice sheet approximately bounded by longitude 60 and 160E, and latitude 80S.  Four traverses operated from Casey during 1981: Autumn (2 months), Winter I (2 weeks), Winter II (3 weeks), and Spring (3.5 months).  Data collected from the traverses included:  * Ice velocity at several stations via the use of JMR Doppler Satellite surveying equipment. * Measurements of snow accumulation. * Surface profiles by barometric levelling * Ice thickness and bedrock profile using ice radar * Horizontal distances along the Undulation Line * 10m depth snow temperatures * Density of surface snow * Snow samples for stable oxygen isotope ration analysis * Regular determinations of gravity  The collected data was collated into a report that is archived at the Australian Antarctic Division.	proprietary
 iagp_interim_survey_1973_1	IAGP Traverse Interim Survey Report 1973	AU_AADC STAC Catalog	1973-01-25	1973-05-24	112, -68, 113, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311124-AU_AADC.umm_json	The Autumn field trip of the 1973 IAGP at Casey carried out a number of surveying operations, recording the location and elevation of a series of snow canes (labelled A001-A015, along B004, B010 and INT A8A9). The report on the trip, a diagram of the snow cane layout, and the results of the survey, are archived at the Australian Antarctic Division. Copies of the raw numbers from the ice radar are also archived separately.	proprietary
 ibis_2_5_808_1	Integrated Biosphere Simulator Model (IBIS), Version 2.5	ORNL_CLOUD STAC Catalog	1994-01-01	1994-01-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2956544820-ORNL_CLOUD.umm_json	The Integrated Biosphere Simulator (or IBIS) is designed to be a comprehensive model of the terrestrial biosphere. Tthe model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). IBIS was developed by Center for Sustainability and the Global Environment (SAGE) researchers as a first step toward gaining an improved understanding of global biospheric processes and studying their potential response to human activity  [Foley et al. 1996].  IBIS was constructed to explicitly link land surface and hydrological processes, terrestrial biogeochemical cycles, and vegetation dynamics within a single, physically consistent framework. Furthermore, IBIS was one of a new generation of global biosphere models, termed Dynamic Global Vegetation Models (or DGVMs), that consider transient changes in vegetation composition and structure in response to environmental change. Previous global ecosystem models have typically focused on the equilibrium state of vegetation and could not allow vegetation patterns to change over time. Version 2.5 of IBIS includes several major improvements and additions [Kucharik et al. 2000]. SAGE continues to test the performance of the model, assembling a wide range of continental- and global-scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO2 flux. Using these field data and model results for the contemporary biosphere (1965-1994), their evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO2 flux agreed reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results [Kucharik et al. 2000].	proprietary
-icbo2020_1.0	ICBO2020	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.6593323, 46.0692292, 6.5931702, 46.5284458	https://cmr.earthdata.nasa.gov/search/concepts/C2789815172-ENVIDAT.umm_json	Ontologies used for the case study in the publication.  Creative Commons (CC) license: CC BY-NC-SA <https://creativecommons.org/licenses/by-nc-sa/3.0/>	proprietary
-ice-nucleating-particle-concentrations-active-at-15-c-at-weissfluhjoch_1.0	Ice nucleating particle concentrations active at -15 °C at Weissfluhjoch	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.806475, 46.832964, 9.806475, 46.832964	https://cmr.earthdata.nasa.gov/search/concepts/C2789815213-ENVIDAT.umm_json	This dataset contains number concentrations of ice-nucleating particles active at -15 °C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title “Towards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling”.	proprietary
 ice-radar-traverse-mirny-domec-1978_1	Ice Radar Traverse Notes, Mirny-Dome C, 1978	AU_AADC STAC Catalog	1978-10-01	1979-02-28	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313566-AU_AADC.umm_json	Notes from the ice radar used on the traverse from Mirny to Dome C in 1978, recording file usage for various locations, and initial observations of ice thickness from the radar.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 ice_movement_mirny-domec_1977-84_1	Ice Movement and Velocity Observations/Calculations, Mirny to Dome C Traverse, 1977-1984	AU_AADC STAC Catalog	1977-01-01	1984-12-31	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313563-AU_AADC.umm_json	Several traverses were completed from Mirny to Dome C by the Russians in the 1970s and 1980s. Precise location records of 15 stations along the traverse were completed by JMR analysis, first in the 1977/78 traverse, and repeated in the 1983/84 traverse. This allowed the calculation of ice movement, and hence ice velocity, to be made for those sites.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 ice_retreat_blooms_1	Images showing the retreat of sea ice and subsequent chlorophyll-a bloom in the spring/summer of 1997/98-2009/10, East Antarctica.	AU_AADC STAC Catalog	1997-11-01	2010-04-01	20, -72, 160, -45	https://cmr.earthdata.nasa.gov/search/concepts/C1214311144-AU_AADC.umm_json	This data set comprises animations showing the spring/summer melt of sea ice in East Antarctica and the subsequent chlorophyll blooms. SMMR-SSM/I sea ice concentration data were obtained from the National Snow and Ice Data Centre, and AMSR-E sea ice concentration data from the University of Bremen. SeaWiFS and MODIS chlorophyll-a data were obtained from the OceanColor site. SeaWiFS and SMMR-SSM/I data were used for seasons prior to 2002/03; MODIS and AMSR-E data were used for later seasons. Chl-a data were averaged over 16-day periods. The animations also show the ETOPO2 bathymetry and the fronts of the Antarctic circumpolar current (Orsi et al. 1995).	proprietary
 icecore_borehole_orientation_1970s_1	Glaciology borehole orientations for ice coring sites, Law Dome, 1970s	AU_AADC STAC Catalog	1974-01-01	1979-12-31	110, -67, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1292614797-AU_AADC.umm_json	Raw orientations obtained from measurements (and re-measurements) from several ice core boreholes on Law Dome.  Holes include SGP (1979), BHQ (1977,1979), SGF (1974, 1977, 1979), SGB (1979) and BHD (1977, 1979).  These documents have been archived at the Australian Antarctic Division.	proprietary
-icecube_microct_snow_grainsize_1.0	IceCube_microCT_Snow_grainsize	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-38.4592, 46.8123672, 9.8472047, 72.5796	https://cmr.earthdata.nasa.gov/search/concepts/C2789815197-ENVIDAT.umm_json	The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.	proprietary
 ikonos_Not provided	IKONOS-2	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567858-USGS_LTA.umm_json	Since its launch in September 1999, GeoEye's IKONOS satellite has provided a reliable stream of image data since January 2000, which has become the standard for commercial high-resolution satellite data products. With an altitude of 681 km and a revisit time of approximately 3 days, IKONOS produces one-meter panchromatic and four-meter multispectral imagery that can be combined to accommodate a wide range of high-resolution imagery applications.	proprietary
-illgraben-debris-flow-characteristics-2019-2022_1.0	Illgraben debris-flow characteristics 2019-2022	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.5961876, 46.2675443, 7.6363564, 46.310011	https://cmr.earthdata.nasa.gov/search/concepts/C3226082518-ENVIDAT.umm_json	List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.  This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.	proprietary
 imergcpex_1	Integrated Multi-satellitE Retrievals for GPM (IMERG) CPEX V1	GHRC_DAAC STAC Catalog	2017-05-24	2017-07-16	-99.95, 5.05, -45.05, 39.95	https://cmr.earthdata.nasa.gov/search/concepts/C2647820404-GHRC_DAAC.umm_json	The Integrated Multi-satellitE Retrievals for GPM (IMERG) CPEX dataset includes measurements gathered by IMERG during the Convective Processes Experiment (CPEX) field campaign. IMERG combines precipitation estimates from multiple passive microwave (PMW) sensors available in a 30-minute analysis time.  These estimates are retrieved using the Goddard Profiling (GPROF) algorithm that converts PMW brightness temperatures to a precipitation estimate. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. IMERG combines information from the GPM satellite constellation to estimate precipitation over the majority of the Earth's surface. Data are available from May 24, 2017 through July 16, 2017 in netCDF-3 format.	proprietary
-imis-measuring-network_1.0	IMIS measuring network	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	6.120228, 45.971755, 10.449316, 47.170837	https://cmr.earthdata.nasa.gov/search/concepts/C3226082559-ENVIDAT.umm_json	The Intercantonal Measurement and Information System (IMIS) consists of nearly 200 automatic measuring stations. They are distributed throughout the Swiss Alps and the Jura region and, in most cases, are situated above the tree line, most frequently between 2000 and 3000 m. The stations record the conditions around the clock, in general every 30 minutes.   Most IMIS stations are located in the vicinity of starting zones of potentially destructive avalanches, and provide essential information to local safety officers for public safety in settlements and on the roads. They are also used for snow-hydrological and research purposes and by the avalanche warning service of the SLF.  This dataset comprises data from IMIS snow and wind stations. The snow and wind stations are usually situated close to each other and measure the key weather data required for assessing the avalanche danger.      ## IMIS snow stations    Snow stations are located on wind-protected flat terrain. The snowpack model SNOWPACK calculates the layers and properties of the snowpack throughout the winter for each of the IMIS snow stations.  The following variables are measured or simulated in the standard programme of IMIS snow stations and are available in this dataset:    - Snow depth  - 24-hour new snow (SNOWPACK simulation) - Air and surface temperature  - Wind speed and direction - Relative humidity - Reflected shortwave radiation - Ground temperature - Snow temperature 25 cm, 50 cm and 100 cm above the ground    ## IMIS wind stations    Wind stations are generally situated at higher altitudes on exposed summits or ridges. The following variables are measured in the standard programme of IMIS wind stations and are available in this dataset:    - Wind speed and direction - Air temperature - Relative humidity        __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__.    __To download live data use our [API](https://measurement-api.slf.ch)__.    __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.	proprietary
-impact-des-extremes-sur-les-scieries_1.0	Impact des événements météorologiques extrêmes sur l'économie forestière suisse: le point de vue des scieries	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815270-ENVIDAT.umm_json	"Extreme events impact on the Swiss forest economy: the sawmill perspective    Supplementary Information  This survey aimed at answering three main questions: (i) What are the Swiss sawmills challenges and actions taken after a large storm/windthrow?, (ii) How do these challenges and actions vary across sawmill size and location?, and (iii) is adaptation from the sawmills to extreme events possible, with regards to wood type, products and required infrastructure?      Informations supplémentaires  Cette enquête visait à répondre à trois questions principales : (i) Quels sont les défis et les mesures prises par les scieries suisses après une grosse tempête ou un coup de vent ? (ii) Comment ces défis et ces mesures varient-ils selon la taille et l'emplacement de la scierie ? et (iii) l'adaptation des scieries aux événements extrêmes est-elle possible, en ce qui concerne le type de bois, les produits et l'infrastructure requise ?  																					  Ziel dieser Umfrage war die Beantwortung von drei Hauptfragen: (i) Welche  s sind die Herausforderungen und Massnahmen der Schweizer Sägewerke nach einem grossen Sturm/Windwurf?, (ii) Wie unterscheiden sich diese Herausforderungen und Massnahmen je nach Grösse und Standort des Sägewerks? und (iii) Ist eine Anpassung der Sägewerke an Extremereignisse möglich, in Bezug auf Holzart, Produkte und erforderliche Infrastruktur?"	proprietary
-impact-of-non-native-tree-species-in-europe-on-soil-properties-and-biodiversity_1.0	Impact of non-native tree species in Europe on soil properties and biodiversity: a review	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-10.7226562, 31.8028926, 45.8789063, 67.1358294	https://cmr.earthdata.nasa.gov/search/concepts/C3226082578-ENVIDAT.umm_json	Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.	proprietary
-impulse_response_function_script_1.2	Impulse response functions for nonlinear nonstationary and heterogeneous systems	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815293-ENVIDAT.umm_json	The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.      Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.	proprietary
 in2018_v05_1	CTD data of cruise in2018_v05 of the RV Investigator	AU_AADC STAC Catalog	2018-10-16	2018-11-16	148.7, -57.3, 156.3, -45.8	https://cmr.earthdata.nasa.gov/search/concepts/C1613498519-AU_AADC.umm_json	Oceanographic measurements were collected aboard RV Investigator cruise in1805 (CSIRO voyage designation in2018_v05) from 16th October to 16th November 2018, along a number of transects across a standing meander of the Antarctic Circumpolar Current between 148o and 156oE. A total of 77 CTD vertical profile stations were taken on the cruise, most to within 12 metres of the bottom. Over 1900 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate, ammonium and nitrite), chlorophyll, POC and DOC, and for incubation experiments, using a 36 bottle rosette sampler. Full depth current profiles were collected by an LADCP attached to the CTD package. Upper water column current profile data were collected by a ship mounted ADCP (75 kHz and 150 kHz). Data coverage was increased by additional transects towing a Triaxus towed CTD system. A microstructure profiler was deployed at many of the CTD stations. Meteorological and water property data were collected by the array of ship's underway sensors. An oceanographic mooring was deployed at 55o 32.544’S , 150o 52.332’E, and a series of floats and drifters were deployed. Bathymetry was collected by the ship’s multibeam system.  The data set contains CTD 2dbar averaged data, and Niskin bottle data (core hydrochemistry of salinity, dissolved oxygen and nutrients), in text and matlab formats, and a full data report. A WOCE (CCHDO) 'exchange' format version of the data is also available on request.	proprietary
 inclinometer_lawdome_70s_1	Ice Core Borehole Inclinometer Readings, Law Dome 1974-79	AU_AADC STAC Catalog	1974-01-01	1979-12-31	110, -67, 115, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311146-AU_AADC.umm_json	A collection of inclinometer readings from various ice core boreholes on Law Dome in the late 1970s. Holes recorded include SGF (1974, 1977 and 1979), SBG, SGP (1979) and BHD (1977 and 1979)  These documents have been archived at the Australian Antarctic Division.	proprietary
-increment-11_1.0	Increment	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815305-ENVIDAT.umm_json	Increase in the volume of stemwood with bark of the trees and shrubs  starting at 12 cm dbh that have survived between two inventories and of the losses (modelled for the half period), plus the volume of the gains. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-increment_star-162_1.0	Increment*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815315-ENVIDAT.umm_json	Increase in the volume of stemwood with bark of the surviving trees and shrubs  starting at 12 cm dbh between two inventories and the losses (modelled for the half period), plus the volume of gains.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-individual-tree-tls-point-clouds-for-tree-volume-estimation_1.0	Individual tree TLS point clouds for tree volume estimation	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.6011658, 47.2560873, 8.6585999, 47.609095	https://cmr.earthdata.nasa.gov/search/concepts/C3226082133-ENVIDAT.umm_json	## Dataset  This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.   The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.  Individual trees were semi-automatically segmented from the co-registered TLS point clouds.    ## Background  Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.  Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.  In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).  Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.	proprietary
-induced-rockfall-dataset-chant-sura_1.0	Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Flüelapass, Grisons, Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.9632928, 46.7401819, 9.9743649, 46.7500628	https://cmr.earthdata.nasa.gov/search/concepts/C2789815108-ENVIDAT.umm_json	The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.     Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:    # Resources (individual zip-archives)    __ExperimentalRuns__:  Archive with all available StoneNode data streams and its respective figure (.mat files)    __Input__: Archive containing folders     * GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,   * UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and   * VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times    __EOTA__: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.    __Output__: Reconstruced trajectory information for all 82 reconstructed trajectories    __Video__: available video streams for all runs      ## Further information   Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:    A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/  P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/    Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019	proprietary
-inishell-2-0-4_2.0.4	Inishell-2.0.4	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815121-ENVIDAT.umm_json	This is the source code of the Inishell-2.0.4 flexible Graphical User Interface. It is configured through an XML file for applications that themselves need to be configured via ini-files. It allows to set constraints regarding the sections, keys and values that may be present in the ini-files that are produced by the end user. It is released under the GPL-v3 or later license. Precompiled binaries are available at https://models.slf.ch/p/inishell-ng/downloads/ while the development takes place at https://code.wsl.ch/snow-models/inishell (gitlab forge).	proprietary
 inpe_CPTEC_GLOBAl_FORECAST_Not provided	Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC)	CEOS_EXTRA STAC Catalog	1970-01-01		-120, -60, 0, 30	https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.umm_json	"CPTEC offers global model analysis and forecast images for twelve         meteorological parameters. Forecast time steps range from the initial         analysis each day out to six days. The user may choose forecasts from         the most recent forecast run back to the previous 36 hours.                  Parameters Forecasted:                  Mean Sea Level Pressure         Temperature at 1000 hPa         Relative Humidity at 925 hPa, 850 hPa         Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa         Velocity Potential at 925 hPa, 200 hPa         Stream Function at 925 hPa, 200 hPa         500/1000 hPa Thickness         Advection of Temperature at 925 hPa, 850 hPa, 500 hPa         Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa         Convergence of Humidity Flux at 925 hPa, 850 hPa         Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa         Total Precipitation Last 24 Hours                  All forecast images can be obtained via World Wide Web from the CPTEC         Home Page.         Link to: ""http://www.cptec.inpe.br/"""	proprietary
-input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0	Input data for break point detection of Swiss snow depth time series	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.umm_json	Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.    ## Columns   Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero	proprietary
-input-data-for-impact-assessment-of-homogenised-snow-series_1.0	Input data for impact assessment of homogenised snow series	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.umm_json	# Input data for the following research article: Impact assessment of homogenised snow depth series on trends  The data consists of separate output files from the following homogenisation methods:  * Climatol  * HOMER  * interpQM  The variable is seasonal mean snow depth (HSavg)    plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude)	proprietary
 insects_subsaharanAfrica_Not provided	A Checklist of the Insects of Subsaharan Africa	SCIOPS STAC Catalog	2000-01-01		13.68, -35.9, 33.98, -21.27	https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json	"One of the most basic needs for inventorying, exploiting and             monitoring the changes in the insect diversity of Africa is a complete             list of species which are already know to occur in             Africa. Surprisingly, such a basic list does not exist, despite some             250 years of formal scientific description of life on earth. The             International Centre of Insect Physiology and Ecology (ICIPE), along             with the National Museum of Natural History, is therefore sponsoring             the production of the list, which will provide a reliable platform of             'standard' names for species on which many other projects depend. This             list, or authority file, will greatly enhance communication both among             scientists and between scientists and users of scientific data. The             African list will also be a major contribution toward the proposed             list of world species (e.g. the Global Biodiversity Information             Facility (GBIF) and Species 2000 initiative of DIVERSITAS).          A demonstration database is provided for the species of the orders             Odonata (dragonflies and damselflies), Ephemeroptera (mayflies),             Plecoptera (stoneflies), Megaloptera (alderflies),             Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers,             planthoppers, scales, and others), and Trichoptera (caddisflies).          Invitation to collaboration: Compilation of the checklist is being             coordinated by Nearctica (formerly Entomological Information             Specialists), because of their experience with Nomina Insecta             Nearctica. We are attempting to collaborate with known specialists as             contributors and reviewers, but we welcome additional suggestions of             collaborators. Inquires can be directed to Scott Miller             (miller.scott@nmnh.si.edu).          Information was obtained from             ""http://entomology.si.edu/""."	proprietary
 instm_trawl_Not provided	National Institute of Marine Sciences and Technologies - Trawling Surveys	CEOS_EXTRA STAC Catalog	1983-04-16	2006-11-03	5.14, 17.1, 13.37, 38.1	https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.umm_json	The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia        has four laboratories.  Regular trawl surveys are done by the Laboratory of        Marine Living Resources to assess the exploitable resource stocks.              This dataset consists of 7664 records of 90 families.	proprietary
-intercomparison-of-photogrammetric-platforms_1.0	Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland)	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.7544861, 46.6485877, 10.0428772, 46.844319	https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.umm_json	"This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper ""Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping"".    __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles.    When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas."	proprietary
-interview-guide-and-transcripts_1.0	Interview guide and transcripts (CONCUR Aim 2 on Governance)	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.umm_json	This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed.	proprietary
-intratrait_1.0	intratrait	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	6.02, -46.64, 178.52, 53.46	https://cmr.earthdata.nasa.gov/search/concepts/C3226082491-ENVIDAT.umm_json	This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.  The following traits and parameters were measured and are available in this data set:  As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.   For more details see Rixen et al. 2022, Journal of Ecology.	proprietary
-inventaire-forestier-national-suisse-2009-2017_1.0	Inventaire forestier national suisse. Résultats du quatrième inventaire 2009-2017	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815279-ENVIDAT.umm_json	Swiss National Forest Inventory. Results of the fourth survey 2009–2017.  The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings.  Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland        Inventaire forestier national suisse. Résultats du quatrième inventaire 2009-2017.  Les relevés du quatrième inventaire forestier national suisse (IFN) ont eu lieu entre 2009 et 2017, en moyenne huit ans après le troisième inventaire. Les résultats sur l’état et l’évolution de la forêt suisse sont présentés et expliqués en détail. Le rapport est structuré thématiquement selon les critères et indicateurs européens pour la gestion durable des forêts : ressources forestières, santé et vitalité, production de bois, diversité biologique, forêt protectrice et socio-économie. L’ouvrage s’achève par un bilan de la durabilité basé sur les résultats de l’IFN.  Mots-clés : surface forestière, volume de bois, accroissement, exploitation, structure de la forêt, état de la forêt, production de bois, biodiversité, forêt protectrice, récréation, durabilité, résultats de l’inventaire forestier national, Suisse       Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf.	proprietary
 islscp2_soils_1deg_1004_1	ISLSCP II Global Gridded Soil Characteristics	ORNL_CLOUD STAC Catalog	1995-01-01	1996-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785271209-ORNL_CLOUD.umm_json	This data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS). The data in this data set were produced by the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) staff from data obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/). See the related data sets section below. Two-dimensional gridded maps of selected soil parameters, including soil texture, at a 1.0 by 1.0 degree spatial resolution and for two soil depths are provided. All data layers have been adjusted to match the ISLSCP II land/water mask. There are 36 data files with this data set.	proprietary
-isotope-lab_1.0	Stable Isotope Research Lab WSL	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.45634, 47.360992, 8.45634, 47.360992	https://cmr.earthdata.nasa.gov/search/concepts/C2789815291-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg ""Isotope Laboratory WSL"")    The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL."	proprietary
 isslis_v2_fin_2	Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data V2	GHRC_DAAC STAC Catalog	2017-03-01	2023-11-16	-180, -55, 180, 55	https://cmr.earthdata.nasa.gov/search/concepts/C2303212754-GHRC_DAAC.umm_json	The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format.	proprietary
 isslisg_v2_fin_2	Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds V2	GHRC_DAAC STAC Catalog	2017-03-01	2023-11-16	-180, -55, 180, 55	https://cmr.earthdata.nasa.gov/search/concepts/C2303219035-GHRC_DAAC.umm_json	The Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Backgrounds dataset was collected by the LIS instrument mounted on the ISS and are used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of quality controlled science data. This data collection can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats.	proprietary
 iziko_Crustaceans_Not provided	iziko South African Museum - Crustacean Collection	CEOS_EXTRA STAC Catalog	1883-01-01	2003-12-31	-97.22, -74.58, 172.7, 34.62	https://cmr.earthdata.nasa.gov/search/concepts/C2232477683-CEOS_EXTRA.umm_json	The iziko South African Museum houses the most important crustacean (crabs, lobsters, shrimps, barnacles) collection in South Africa.  Significant past contributions were made by K.H. Barnard,  J.R. Grindley and B.F. Kensley (Crustacea).   It currently contains 5101 records of 274 families.	proprietary
 iziko_molluscs_Not provided	iziko South African Museum - Mollusc Collection	CEOS_EXTRA STAC Catalog	1881-01-01	2000-12-31	-159.56, -59.45, 165.95, 50.6	https://cmr.earthdata.nasa.gov/search/concepts/C2232477686-CEOS_EXTRA.umm_json	The iziko South African Museum's mollusc collection of southern        African species is the second largest mollusc collection in southern        Africa. Significant additions were made in the past by K.H. Barnard.              It currently contains 6078 records.  The families were not provided.	proprietary
 iziko_sharks_Not provided	iziko South African Museum - Shark Collection	CEOS_EXTRA STAC Catalog	1828-04-01	2005-12-31	179.73, 62, 179.25, 73.3	https://cmr.earthdata.nasa.gov/search/concepts/C2232477682-CEOS_EXTRA.umm_json	This collection has global holdings. It includes numerous representatives of       eight of the shark groups, most representatives of the Batoids and Chimaeras,       including rare species. Significant material is being acquired from, fisheries       research and tooth fish long-lining and fishing company by-catches.	proprietary
 jetty_sat_1	Jetty Peninsula Satellite Image Map 1:500 000	AU_AADC STAC Catalog	1991-09-01	1991-09-30	65, -72, 73, -70	https://cmr.earthdata.nasa.gov/search/concepts/C1214313527-AU_AADC.umm_json	Satellite image map of Jetty Peninsula, Mac. Robertson Land, Antarctica. This map is part (d) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991.  The map is at a scale of 1:500000, and was produced from Landsat TM and Landsat MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows traverses/routes/foot tracks, glaciers/ice shelves, and stations/bases. The map has only geographical co-ordinates.	proprietary
-jfetzer-phosphatase-leaching_1.0	Phosphatase leaching	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	7.8588858, 49.9488636, 13.7036124, 53.3024328	https://cmr.earthdata.nasa.gov/search/concepts/C2789815303-ENVIDAT.umm_json	Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate.   Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany   of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest.   Soil samples were taken in July 2019. For methods see publication.	proprietary
 jornada_albedo_667_1	PROVE Surface albedo of Jornada Experimental Range, New Mexico, 1997	ORNL_CLOUD STAC Catalog	1997-05-22	1997-05-27	-106.75, 32.5, -106.75, 32.5	https://cmr.earthdata.nasa.gov/search/concepts/C2804796522-ORNL_CLOUD.umm_json	The objective of this study was to determine the spatial variations in field measurements of broadband albedo as related to the ground cover and under a range of solar conditions during the Prototype Validation Exercise (PROVE) at the Jornada Experimental Range in New Mexico on May 20-30, 1997.	proprietary
 jornada_canopy_brf_668_1	PROVE Vegetation Reflectance of Jornada Experimental Range, New Mexico, 1997	ORNL_CLOUD STAC Catalog	1997-05-23	1997-05-28	-106.75, 32.5, -106.75, 32.5	https://cmr.earthdata.nasa.gov/search/concepts/C2804797176-ORNL_CLOUD.umm_json	Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997.	proprietary
 jornada_landcover_lai_665_1	PROVE Land Cover and Leaf Area of Jornada Experimental Range, New Mexico, 1997	ORNL_CLOUD STAC Catalog	1997-05-13	1997-05-31	-106.75, 32.5, -106.75, 32.5	https://cmr.earthdata.nasa.gov/search/concepts/C2804794793-ORNL_CLOUD.umm_json	Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the PROVE exercise on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover.	proprietary
@@ -18024,7 +17559,6 @@ kgyximpacts_1	KGYX NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-0
 kilnimpacts_1	KILN NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-89.1706, 35.2906, -78.4723, 43.5504	https://cmr.earthdata.nasa.gov/search/concepts/C2030432039-GHRC_DAAC.umm_json	The KILN NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 kilximpacts_1	KILX NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-94.7433, 36.0206, -83.9303, 44.2804	https://cmr.earthdata.nasa.gov/search/concepts/C2030434636-GHRC_DAAC.umm_json	The KILX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 kindimpacts_1	KIND NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-91.6517, 35.5776, -80.9086, 43.8414	https://cmr.earthdata.nasa.gov/search/concepts/C2030436692-GHRC_DAAC.umm_json	The KIND NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
-kinetic-experiments-on-the-oxidation-of-bromide-by-ozone-from-289-245-k_1.0	Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.2040405, 47.5223016, 8.2610321, 47.5371377	https://cmr.earthdata.nasa.gov/search/concepts/C2789815314-ENVIDAT.umm_json	The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.	proprietary
 kiwximpacts_1	KIWX NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-91.2062, 37.2284, -80.1938, 45.4887	https://cmr.earthdata.nasa.gov/search/concepts/C2030440758-GHRC_DAAC.umm_json	The KIWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 kjklimpacts_1	KJKL NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-88.527, 33.461, -78.099, 41.721	https://cmr.earthdata.nasa.gov/search/concepts/C2012922051-GHRC_DAAC.umm_json	The KJKL NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 klotimpacts_1	KLOT NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-93.612, 37.474, -82.557, 45.735	https://cmr.earthdata.nasa.gov/search/concepts/C2012927437-GHRC_DAAC.umm_json	The KLOT NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. This Level II dataset contains meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
@@ -18053,14 +17587,9 @@ kt_veg_inventory_776_1	SAFARI 2000 Vegetation Cover Characteristics, Kalahari Tr
 kt_woody_veg_777_1	SAFARI 2000 Woody Vegetation Characteristics of Kalahari and Skukuza Sites	ORNL_CLOUD STAC Catalog	2000-02-29	2000-06-25	21.71, -31.5, 25.5, -15.44	https://cmr.earthdata.nasa.gov/search/concepts/C2789101524-ORNL_CLOUD.umm_json	This data set contains species composition, basal area, height, and crown cover of all woody plants at six sites along the Kalahari Transect visited in February-March of 2000 as part of SAFARI 2000. Similar measurements on woody and herbaceous vegetation at the Skukuza Flux Tower site in Kruger National Park, South Africa, were made in June of 2000. Leaf area index was derived from measurements made using PAR sensors at each site.Sampling protocol was the same at each site, with a slight variation at Skukuza.  A grid of 42 points, 6 rows of 7 columns, each 50 m apart, was laid down in an area 300 m x 350 m for the Kalahari Transect sites. At Skukuza, the grid was 7x7, or 350 m x 350 m, centered on the tower site, yielding 49 points. At each grid point, all woody plants within a circular plot of a fixed radius were identified and measured. Stem circumference was measured on all stems and basal area per stem was derived. Basal area for the circular plots, per species, was calculated and extrapolated to hectares. Tree and stem densities were determined from the number of trees and stems in subplots and extrapolated to hectares. Woody plant height and canopy cover were determined, and aboveground woody biomass and peak leaf area index were estimated. The files are in comma-delimited ASCII format, with the first line listing the data set, author, and date, followed by the data records.	proprietary
 ktyximpacts_1	KTYX NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-81.4022, 39.6256, -69.9573, 47.8857	https://cmr.earthdata.nasa.gov/search/concepts/C2020264637-GHRC_DAAC.umm_json	The KTYX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 kvwximpacts_1	KVWX NEXRAD IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-03-01	-92.9872, 34.1302, -82.4619, 42.3903	https://cmr.earthdata.nasa.gov/search/concepts/C2020265507-GHRC_DAAC.umm_json	The KVWX NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected at 31 NEXRAD sites from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms  (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
-l-band-davos-laret_1.0	L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8748744, 46.8475483, 9.8748744, 46.8475483	https://cmr.earthdata.nasa.gov/search/concepts/C2789815292-ENVIDAT.umm_json	"Dataset from the publication ""L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory"", under review in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. volume and issue TBD.     Dataset specifics are described in the publication."	proprietary
-labchemistrymetamorphism_1.0	Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.2224941, 47.5363844, 8.2224941, 47.5363844	https://cmr.earthdata.nasa.gov/search/concepts/C2789815065-ENVIDAT.umm_json	Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.	proprietary
-labes_1.0	LABES 2 Indicators of the Swiss Landscape Monitoring Program	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082114-ENVIDAT.umm_json	The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception.   This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES.	proprietary
 lai_45_1	Leaf Area Index Data (OTTER)	ORNL_CLOUD STAC Catalog	1991-05-13	1991-05-15	-123.27, 44.29, -121.33, 44.67	https://cmr.earthdata.nasa.gov/search/concepts/C2804754747-ORNL_CLOUD.umm_json	LAI estimates computed from unweighted openness by the CANOPY program from digitized canopy photographs	proprietary
-lake_cc_scenarios_ch2018_1.0	Lake climate change scenarios CH2018	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082136-ENVIDAT.umm_json	"The dataset ""Lake_climate_change_scenarios_CH2018"" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled ""Lower summer lake levels in regulated perialpine lakes, caused by climate change,"" authored by Wechsler et al. in 2023.    The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun).    In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes."	proprietary
 lake_erie_aug_2014_0	2014 Lake Erie measurements	OB_DAAC STAC Catalog	2014-08-18		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json	2014 Lake Erie measurements.	proprietary
 lambert_geology_gis_1	Geology of the Lambert Glacier - Prydz Bay Region GIS Dataset	AU_AADC STAC Catalog	1980-01-01	1997-12-31	58, -76, 78, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313571-AU_AADC.umm_json	This dataset is the GIS data used for the map 'Geology of the Lambert Glacier - Prydz Bay Region, East Antarctica'  published by the Australian Geological Survey Organisation in January 1998.  The data is in three formats: ArcInfo interchange, ArcInfo coverage and shapefile.  A document is included with further information about the data. The map is available from a URL in this metadata record.	proprietary
-land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0	Land use/cover dynamics in Austin metropolitan area since 1992	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-97.7014167, 30.3732703, -97.7014167, 30.3732703	https://cmr.earthdata.nasa.gov/search/concepts/C2789815150-ENVIDAT.umm_json	The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.	proprietary
 land_cover_data-1km_627_1	SAFARI 2000 Land Cover from AVHRR, 1-km, 1992-1993 (Hansen et al.)	ORNL_CLOUD STAC Catalog	1992-01-01	1993-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788343294-ORNL_CLOUD.umm_json	This data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available.	proprietary
 land_cover_data_1deg_677_1	LBA Regional Land Cover from AVHRR, 1-Degree, 1987 (Defries and Townshend)	ORNL_CLOUD STAC Catalog	1987-01-01	1987-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2777325206-ORNL_CLOUD.umm_json	This data set consists of a subset of a 1-degree global land cover product (DeFries and Townshend 1994). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America. The data are in ASCII GRID format.	proprietary
 land_cover_data_1km_678_1	LBA Regional Land Cover from AVHRR, 1-km, 1992-1993 (Hansen et al.)	ORNL_CLOUD STAC Catalog	1992-01-01	1993-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2777325367-ORNL_CLOUD.umm_json	"This data set is a subset of Hansen et al. (1999), ""1 km Global Land Cover Data Set Derived from AVHRR,"" which was developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID file format.In recent years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, LGRSS researchers have employed the NASA/NOAA Pathfinder AVHRR Land (PAL) data set with a spatial resolution of 1 km. The PAL data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms. The PAL data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The LGRSS researchers' aim was to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.The 1-km global land cover product was created from 1992-1993 local area coverage (LAC) AVHRR data. The global land cover product is available for download from the University of Maryland's Global Land Cover Facility (GLCF) Web site (http://glcf.umiacs.umd.edu/data/landcover/index.shtml). Forty-one metrics were developed to describe global vegetation phenology, and these data were used to make the 1-km land cover map. The final product contains 13 land cover classes.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/land_cover_data_1km/comp/glcf1km_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html."	proprietary
@@ -18072,12 +17601,6 @@ land_surf_ref_l4-5_Not provided	Land Surface Reflectance - L4-5	USGS_LTA STAC Ca
 land_surf_ref_l7_etm_Not provided	Land Surface Reflectance - 17-ETM+	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567906-USGS_LTA.umm_json	The surface reflectance CDR is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). LEDAPS was originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs)grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006). The software applies Moderate Resolution Imaging spectroradiometer (MODIS) atmospheric correction routines to Level-1 Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+)data.  Water,vapor, ozone, geopotential height, aerosol optical thickness,and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA)reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water.  The result is delivered as the Landsat surface reflectance CDR.	proprietary
 landcover_bvoc_est_764_1	SAFARI 2000 Estimated BVOC Emissions for Southern African Land Cover Types	ORNL_CLOUD STAC Catalog	2001-01-01	2001-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2789072937-ORNL_CLOUD.umm_json	Improved vegetation distribution and emission data for Africa south of the equator were developed for the Southern African Regional Science Initiative (SAFARI 2000) and combined with biogenic volatile organic compound (BVOC) emission measurements to estimate BVOC emissions for the southern African region. BVOC emissions were estimated for southern Africa on a monthly basis over a one-year period by combining GIS layers of vegetation, LAI, and climate with a biogenic emissions model, GLOBEIS (Guenther et al, 1993; Guenther, 1999).	proprietary
 landsat-2_NA	Landsat Collection 2 - Level-2	INPE STAC Catalog	2008-01-01	2024-06-14	-75.6664631, -35.6706264, -27.6837564, 6.8339121	https://cmr.earthdata.nasa.gov/search/concepts/C3108204618-INPE.umm_json	Landsat Collection 2 Level-2 Science Products (https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected surface reflectance (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and surface temperature (https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present. This dataset represents the Brazilian archive of Level-2 data from Landsat Collection 2 (https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the Thematic Mapper (https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the Enhanced Thematic Mapper (https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and Operatational Land Imager (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and Thermal Infrared Sensor (https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF (https://www.cogeo.org/) format.	proprietary
-landscape-technology-fit-public-evaluation_1.0	Hybrid choice modelling dataset for the effects of landscape-technology fit on public evaluations	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815133-ENVIDAT.umm_json	We present stated preference data based on a national representative Swiss online panel survey for the preference of renewable energy infrastructure in landscapes. The data was collected between November 2018 to March 2019 using an online questionnaire and resulted on 1026 responses. The online questionnaire consisted of two main parts – (1) questions covering meanings related to landscapes, nature and renewable energy infrastructure, including the “fit” of landscape/REI combinations and (2) online choice experiment. While in the first part of the questionnaire we asked respondents about their personal connection to certain landscapes, to nature and to specific renewable energy infrastructures, we also asked them to evaluate the fitting of seven different Swiss landscapes (near natural alpine areas, northern alps, touristic alpine areas, agricultural plateau, urban plateau, jura ridges, urban alpine valley) with five different REI (wind, PV ground, PV roof, power lines) combinations. In the second part of the questionnaire, the stated choice experiment confronted respondents with 15 consecutive choice tasks, with each task involving a choice between two “energy system transformation” options and an opt-out option (none). Each choice option (beside the opt-out option) included four unlabeled attributes (landscape, wind energy infrastructure, photovoltaic energy infrastructure, high voltage overhead power line infrastructure) with varying levels. Due to data cleaning procedures (item nonresponse) the number of responses used within hybrid choice modelling and analysis was n=844 (12660 choice observations). An analysis of the hybrid choice model and further insights are presented in the article “How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model.”	proprietary
-landscape_1.0	Landscape in contemporary strategic spatial plans of European Urban regions	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-18.4570312, 34.9264749, 30.7617187, 67.7614772	https://cmr.earthdata.nasa.gov/search/concepts/C2789815118-ENVIDAT.umm_json	"The present dataset is part of the published scientific paper Hersperger, A.M., Bürgi, M., Wende, W., Bacău, S. and Grădinaru, S.R., 2020. Does landscape play a role in strategic spatial planning of European urban regions?. Landscape and Urban Planning, 194, p.103702.    The goal of this research was to assess the role of landscape in contemporary strategic spatial planning.  In order to assess the role of “landscape” in the strategic spatial plans, we focused on how plans took advantage of landscape’s integrative power, how plans are based on knowledge on functioning of landscape systems, and how plans show the contribution of landscapes to human well-being. For each aspect, a number of items (detailed in Table 1 of the paper) were selected to assist the assessment.   This study is based on content analysis of the strategic spatial plans of 18 European urban regions. The strategic spatial plans were retrieved from the planning authorities’ websites.  The cases study regions as well as the analyzed strategic spatial plans are presented in Table 2 of the paper.   The authors developed a protocol containing  28 items, out of which 16 were directly derived from information presented in Table 1. As a result, we provide the following outputs:  •	Protocol_items.docx – freely available - Detailed description of all the protocol items used to conduct the analysis.   •	Coding results.xlsx – available on request - Results of the coding procedure. Data were used to create Figures 2, 3, 4, 5, 6 and to qualitatively present the results in the research paper."	proprietary
-large-scale-hazard-indication-simulations-for-avalanches-canton-of-grisons_1.0	Large Scale Hazard Indication Simulations for avalanches, canton of Grisons	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.6511167, 46.1701851, 10.4920496, 47.0651481	https://cmr.earthdata.nasa.gov/search/concepts/C2789815169-ENVIDAT.umm_json	We developed a workflow to generate Large Scale Hazard Simulations for avalanches based on digital elevation models and information on the protective function of the forest. This datasets contains the potential avalanche release areas (PRA) as polygons, the simulation outputs (maximum pressure, maximum flow velocity and maximum flow height) as .tif rasters and the outlines of the simulated avalanches (polygon) for the entire area of the canton of Grisons (7105 km2). The simulations are performed for the scenarios wit return periods of 10, 30, 100 and 300 years, once with (FOR) and once without (NoFor) taking the effect of the forest into account.    The details can be found in this publication:    Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on state wide scale, Nat. Hazards Earth Syst. Sci. Discuss., 2022, 1-22, 10.5194/nhess-2022-11, 2022.	proprietary
-large-scale-risk-assessment-on-snow-avalanche-hazard-in-alpine-regions_1.0	Large-scale risk assessment on snow avalanche hazard in alpine regions	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.5205841, 46.6437796, 8.6819458, 46.8977391	https://cmr.earthdata.nasa.gov/search/concepts/C3226082375-ENVIDAT.umm_json	Potential release files and the artificial RAMMS avalanche simulation output files as well as exposure geodataframe for the case study region of Ortner et al. 2022. Furthermore, all the necessary files to run the risk model Climada Avalnache which code is located at https://github.com/CLIMADA-project/climada_papers.	proprietary
-large-scale-urban-development-projects-in-european-urban-regions_1.0	lsUDPS Large-scale urban development projects in European urban regions	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-15.1171875, 38.2726885, 19.1601563, 62.554207	https://cmr.earthdata.nasa.gov/search/concepts/C2789815221-ENVIDAT.umm_json	"Table of Content: 1. General context of the data set ""lsUDPs"" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation    1. General context of the data set ""lsUDPs""  The data set ""lsUDPs"" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).    2. Background and aims of the study using the data set lsUDPs  As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.  Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).  Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.  Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.  The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.  We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).  The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?  The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.  The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans     3. The data set lsUDPs          3.1 Selection of cases and data collection  To generate the current data set on large-scale urban development projects in European urban regions (data set ""lsUDPs""), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.  In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.           3.2 The data set lsUDPs: Data management and operationalisation  Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.  For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).   The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data."	proprietary
-large-wood-event-database_1.0	Large wood event database	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815250-ENVIDAT.umm_json	In the context of the WoodFlow project (https://woodflow.wsl.ch), an extensive database was developed which documents recruited and transported quantities of large wood (woody debris) together with the associated catchment and flood-specific parameters. Transported large wood volumes were related to catchment area, forest cover, stream length, peak discharge, runoff volume, sediment load, and precipitation. The dataset covers flood events mostly from Switzerland, but also from other alpine catchments in Germany, Italy France and Japan.	proprietary
 lars_christ_sat_1	Lars Christensen Coast Satellite Image Map 1:500 000	AU_AADC STAC Catalog	1991-09-01	1991-09-30	66, -70, 73, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313572-AU_AADC.umm_json	Satellite image map of Lars Christensen Coast, Mac. Robertson Land, Antarctica. This map is part (b) of a series of four north Prince Charles Mountains maps. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991.  The map is at a scale of 1:500000, and was produced from Landsat TM and MSS scenes. It is projected on a Lambert Conformal Conic projection, and shows glaciers/ice shelves, penguin colonies, refuges/depots. The map has only geographical co-ordinates.	proprietary
 lars_geology_1	Geology of the Larsemann Hills 1997	AU_AADC STAC Catalog	1997-02-02	1997-02-02	75.95, -69.48, 76.55, -69.33	https://cmr.earthdata.nasa.gov/search/concepts/C1214313587-AU_AADC.umm_json	Geology of the Larsemann Hills, Antarctica.  Geological data from C.J.Carson, University of Melbourne and K.St&amp;uuml;we, University of Adelaide. Additional interpretation by D.E.Thost, AGSO (now Geoscience Australia). This dataset comprises only the lithology. There has been no attempt to give the ages of the lithological units. This data are displayed in a draft map published in January 1997 (see link below).	proprietary
 lars_geology_2004_1	Geology Map of the Larsemann Hills 2004	AU_AADC STAC Catalog	2003-11-16	2004-02-04	75.95, -69.48, 76.55, -69.33	https://cmr.earthdata.nasa.gov/search/concepts/C1214311148-AU_AADC.umm_json	The Larsemann Hills region is dominated by two major lithological associations, a Palaeoproterozoic felsic/mafic orthogneiss complex (Sostrene Orthogneiss) which occurs as basement to a sequence of pelitic, psammitic and felsic paragneiss (supergroup = Brattstrand Paragneiss) and felsic intrusives. The depositional age of the Brattstrand Paragneiss sequences are controversial but isotopic data suggest derivation from the basement Sostrene Orthogneiss. Current geochronology indicates that the region experienced medium to low pressure granulite-facies metamorphism during the Early Palaeozoic (~500 Ma). Although the paragneiss sequences record no evidence of earlier metamorphism, relicts of a previous metamorphic event at ~1000 Ma are preserved in the Sostrene Orthogneiss. Within the Larsemann Hills region, the Early Palaeozoic event is characterised by peak metamorphism of ~7 kbar at ~800-850 degrees C, with the post-peak evolution characterised by decompression, with some cooling, to 4 kbars at 750 degrees C, then to 2-3 kbar at 600-650 degrees C during final stages of orogenesis, with exhumation largely driven by crustal extension. Tectonic models generally argue for a continental-continental collisional scenario, with thermal input derived from a thinned mantle lithosphere.  See the document available for download at the provided URL for further information.	proprietary
@@ -18085,8 +17608,6 @@ larsemann_envmanagement_maps_1	Larsemann Hills GIS data update from various sour
 larsemann_hills_dem_1	Digital Elevation Model of Larsemann Hills, Antarctica	AU_AADC STAC Catalog	1998-02-01	1998-02-01	75.9408, -69.4816, 76.519, -69.3424	https://cmr.earthdata.nasa.gov/search/concepts/C1611351292-AU_AADC.umm_json	A Digital Elevation Model (DEM) of the Larsemann Hills with cell size 10 metres was interpolated from input coastline, contour, spot height (point locations with an elevation attribute) and lake data from the dataset described by the metadata record 'Larsemann Hills - Mapping from aerial photography captured February 1998' with Entry ID: gis135. The input data for the islands and much of the continent coastal area is estimated to have horizontal accuracy of about 5 metres and vertical accuracy of about 5 metres. The input data for a mainly inland area in the south-east of the data coverage is estimated to have horizontal accuracy of about 15 metres and vertical accuracy of about 15 metres. The spatial coverage for these two categories of input data is shown in a map linked to this metadata record. The interpolation was done using the Topo to Raster tool in ArcGIS. In the interpolation process all cells within a lake are assigned to the minimum elevation value of all cells along the shoreline. i.e. the interpolation is flat across the lake. The output DEM was clipped to the extents of the input data. The dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool. The DEM is stored in the UTM Zone 43S projection. The horizontal datum is WGS84. The vertical datum is Mean Sea Level. The DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade. The DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record: 1 A geotiff; and  2 An ascii file in ESRI's ascii format for rasters.	proprietary
 larsemann_sat_1	Larsemann Hills Satellite Image Map 1:25000	AU_AADC STAC Catalog	1990-08-01	1990-08-31	75.971, -69.489, 76.411, -69.324	https://cmr.earthdata.nasa.gov/search/concepts/C1214313531-AU_AADC.umm_json	Satellite image map of Larsemann Hills, Princess Elizabeth Land, Antarctica. This map (edition 2) was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. The map is at a scale of 1:25000, and was produced from a multispectral SPOT 1 - HRV 2 scene (WRS K278 J495), acquired 19 February 1988. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
 larsemann_visible_disturbance_1	Annotated maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica	AU_AADC STAC Catalog	1990-01-01	2000-05-09	76.07, -69.47, 76.42, -69.37	https://cmr.earthdata.nasa.gov/search/concepts/C1214313590-AU_AADC.umm_json	Annotated large format maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica. The compilation was done by Ewan McIvor of the Australian Antarctic Division and based on discussions with scientists Jim Burgess and Andy Spate. Included are locations and notes relating to: 1 walking and vehicular routes; 2 helicopter landing sites; 3 a tide gauge; 4 a fuel line; 5 a grave site; 6 a long term micro erosion monitoring site established in 1990 by Burgess and Spate; 7 two ice caves; and 8 a pliocene deposit.	proprietary
-larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0	Larval food composition of four wild bee species in five European cities	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	0.2197266, 46.890732, 28.3886719, 59.0864909	https://cmr.earthdata.nasa.gov/search/concepts/C2789815269-ENVIDAT.umm_json	Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.	proprietary
-latent-reserves-in-the-swiss-nfi_1.0	'Latent reserves' within the Swiss NFI	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json	"The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*.           **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database:           * Shrub forests were excluded.  * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested.  * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994).  * Plots had to originate from natural regeneration.   * Intensive livestock grazing must never have occurred on the plots.          The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).    ****    The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper.     The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement."	proprietary
 law_dome_1977_1	Law Dome Field Logs And Strain Grid Results, 1977	AU_AADC STAC Catalog	1977-03-16	1977-12-14	110, -70, 114, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311164-AU_AADC.umm_json	In 1977 several traverses were carried out over the Law Dome area, primarily to drill new ice cores on the dome. The 1974 drill site (near Cape Folger) was redrilled to add instrumentation for inclination, while additional holes at BHQ (418m) and the dome summit (475m, 2x 30m) were also drilled.  In addition to the drilling work, two strain grids were laid out on the ice surface, and the grid laid out in 1974 was remeasured.  Notes on the traverse and drilling (but few results) are contained in this record, along with the results of the strain grid surveys. Records for this work have been archived at the Australian Antarctic Division.   Logbook(s):  Glaciology Log of 1977 Field Work	proprietary
 law_dome_700yr_ion_chem_2	700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica	AU_AADC STAC Catalog	1988-01-01	2000-03-06	112.8, -66.76, 112.86, -66.7	https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json	A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main.  This work was completed as part of ASAC project 757 (ASAC_757).  Species which have been the subject of publication and could be made available after consultation:  Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full)      NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'.  If you need to use this, please contact Mark Curran for help.  An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012.	proprietary
 law_dome_700yr_na_1	700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome	AU_AADC STAC Catalog	1301-01-01	1995-12-31	112.806946, -66.76972, 112.806946, -66.76972	https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json	This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below.	proprietary
@@ -18123,18 +17644,12 @@ leafchem_421_1	Leaf Chemistry, 1992-1993 (ACCP)	ORNL_CLOUD STAC Catalog	1992-06-
 leafspec_424_1	Visible and Near-Infrared Leaf Reflectance Spectra, 1992-1993 (ACCP)	ORNL_CLOUD STAC Catalog	1992-06-18	1993-05-27	-122.05, 37.4, -68.74, 45.22	https://cmr.earthdata.nasa.gov/search/concepts/C2776851335-ORNL_CLOUD.umm_json	Visible/NIR reflectance spectra data for both fresh and dry leaf samples were collected to determine the relationship of foliar chemical concentrations with reflectance.	proprietary
 leemans_cramer_681_1	LBA Regional Mean Climatology, 0.5-Deg, 1930-1960, V. 2.1 (Cramer and Leemans)	ORNL_CLOUD STAC Catalog	1931-01-01	1960-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2776935499-ORNL_CLOUD.umm_json	This data set is a subset of Cramer and Leemans' (2001) global database of mean monthly climatology, which contains monthly averages of mean temperature, temperature range, precipitation, rain days, and sunshine hours for terrestrial areas during 1931-1960. This subset was created for the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are presented at 0.5-degree latitude/longitude resolution in ASCII GRID file format. Cramer and Leemans (2001, Version 2.1) constituted a major update of an earlier database, Leemans and Cramer (1991). The new version was generated from a larger database by means of the partial thin-plate splining algorithm developed by Michael F. Hutchinson, Canberra (Hutchinson and Bischof 1983). Version 2.1 has been used widely, notably by all groups participating in the International Geosphere-Biosphere Programme's Net Primary Productivity (NPP) model intercomparison (Olsen et al. 2001).More information about the data can be found at ftp://daac.ornl.gov/data/lba/physical_climate/leemans_cramer/comp/cramer_lmns_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.	proprietary
 legal_amazon_mask_671_1	LBA Regional Boundary for the Legal Amazon of Brazil, 8-km	ORNL_CLOUD STAC Catalog	1991-01-01	1999-12-31	-80, -34, -34.03, 6.02	https://cmr.earthdata.nasa.gov/search/concepts/C2776929056-ORNL_CLOUD.umm_json	 The Legal Amazon of Brazil is defined by law to include the states of Acre, Amapa, Amazonas, Para, Rondonia, Roraima, Mato Grosso, Maranhao, and Tocantins [Fundacao Instituto Brasileiro de Geografia e Estatistica (IBGE) 1991]. This is the definition used in generating the Legal Amazon mask. The 8-km Legal Amazon mask was generated by Christopher Potter at the Ecosystem Science and Technology Branch of the Earth Science Division at NASA Ames Research Center (Potter and Brooks-Genovese 1999). The mask was generated from the Digital Chart of the World available from Environmental Systems Research Institute, Inc. (ESRI). The mask is available in ASCII GRID format. The README file accompanying the mask has more information regarding data format. More information can be found at ftp://daac.ornl.gov/data/lba/human_dimensions/legal_amazon_mask/comp/legamazon_readme.pdf.	proprietary
-length_of_forest_edge-8_1.0	Length of forest edge	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815302-ENVIDAT.umm_json	Length of the forest edge calculated on the basis of the forest boundary lines determined in the aerial photo.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-length_of_forest_roads-78_1.0	Length of forest roads	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815312-ENVIDAT.umm_json	The length of forest roads corresponds to the length of the NFI forest roads. This length was calculated according to the method of the specific NFI concerned.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 level_1_annual_co2_895_1	TransCom 3: Annual Mean CO2 Flux Estimates from Atmospheric Inversions (Level 1)	ORNL_CLOUD STAC Catalog	1992-01-01	1996-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776864640-ORNL_CLOUD.umm_json	The Atmospheric Tracer Transport Model Intercomparison Project (TransCom) was created to quantify and diagnose the uncertainty in inversion calculations of the global carbon budget that results from errors in simulated atmospheric transport, the choice of measured atmospheric carbon dioxide data used, and the inversion methodology employed. Under the third phase of TransCom (TransCom 3), surface-atmosphere CO2 fluxes were estimated from an intercomparison of 16 different atmospheric tracer transport models and model variants in order to assess the contribution of uncertainties in transport to the uncertainties in flux estimates for annual mean, seasonal cycle, and interannual inversions (referred to as Level 1, 2, and 3 experiments, respectively).This data set provides the model output and inversion results for the TransCom 3, Level I annual mean inversion experiments. Annual mean CO2 concentration data (GLOBALVIEW-CO2, 2000) were used to estimate CO2 sources. The annual average fluxes were estimated for the 1992-1996 period using each of the 16 transport models and a common inversion set-up (Gurney et al., 2002). Methodological choices for this control inversion were selected on the basis of knowledge gained from a wide range of sensitivity tests (Law et al., 2003). Gurney et al. (2003) present results from the control inversion for individual models as well as results from a number of sensitivity tests related to the specification of prior flux information. Additional information about the experimental protocol and results is provided in the companion files and the TransCom project web site (http://www.purdue.edu/transcom/index.php).The results of the Level 1 experiments presented here are grouped into two broad categories: forward simulation fields and response functions (model output) and estimated fluxes (inversion results).	proprietary
 level_2_seasonal_co2_1198_1	TransCom 3: Seasonal CO2 Flux Estimates from Atmospheric Inversions (Level 2)	ORNL_CLOUD STAC Catalog	1990-01-01	1996-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2776866990-ORNL_CLOUD.umm_json	This data set provides model outputs and seasonal mean CO2 fluxes from the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3), Level 2 inversion experiment. Inversion methods can be used to estimate surface CO2 fluxes from atmospheric CO2 concentration measurements, given an atmospheric transport model to relate the two. This Level 2 experiment inverted for the spatial and temporal pattern of the residual CO2 sources and sinks. There were 12 atmospheric tracer transport models utilized in this experiment. The data inverted were mean CO2 concentration data from 75 sites from the GLOBALVIEW-CO2 2000 data set for the period 1992-1996. The seasonal inversion consists of a 3 year forward simulation (365 days per year) containing 4 presubtracted tracers, 11 SF6 tracers, and 22 CO2 tracers (11 terrestrial, 11 oceanic) (Gurney et al., 2000). Carbon fluxes were estimated for each month of an average year determined as the mean of the 1990-1996 time period from an intercomparison of 12 different atmospheric tracer transport models. This data set provides input data, model output data,the cyclo inversion code, a basis function map, and estimated fluxes.	proprietary
 lgbt_daily_logs_1	Lambert Glacier Basin Traverse, Daily Logs 1989-1995	AU_AADC STAC Catalog	1989-01-01	1995-12-31	54, -77, 82, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311135-AU_AADC.umm_json	The Lambert Glacier Basin Traverse program ran from the summer of 1989-90 to the summer of 1994-95. The aim of the program was to take multi-year measurements on the dynamics of the ice-sheet draining into the Lambert Glacier, from around the 2500m ice surface elevation contour. These measurements were then used in mass balance calculations for the whole Lambert-Amery system.  Daily logs were kept for each traverse detailing activities carried out, distance travelled, and in many cases including a copy of the daily SITREP. Logs occasionally recorded other observations (weather, etc), but in most cases scientific data was recorded in other logbooks.  These logbooks have been archived at the Australian Antarctic Division.	proprietary
 lgbt_reports_1	Lambert Glacier Basin Traverse Operations Reports, 1989-1995	AU_AADC STAC Catalog	1989-01-01	1995-12-31	54, -77, 82, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311158-AU_AADC.umm_json	The Lambert Glacier Basin Traverse program ran from the summer of 1989-90 to the summer of 1994-95. The aim of the program was to take multi-year measurements on the dynamics of the ice-sheet draining into the Lambert Glacier, from around the 2500m ice surface elevation contour. These measurements were then used in mass balance calculations for the whole Lambert-Amery system.  Annual reports were written during this program, detailing the activities and science carried out, equipment and personnel used, travel logs, fuel consumption, and problems encountered. These reports have been archived at the Australian Antarctic Division.	proprietary
-lidar-davos-wolfgang_1.0	LIDAR Davos Wolfgang	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789815358-ENVIDAT.umm_json	A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.	proprietary
-lidar-wind-profiler-data_1.0	Wind LIDAR Davos Wolfgang	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789815339-ENVIDAT.umm_json	Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.	proprietary
 lidar_6	Lidar Studies of Atmospheric Structure, Dynamics and Climatology	AU_AADC STAC Catalog	2001-02-07		77.95, -68.58, 77.97, -68.56	https://cmr.earthdata.nasa.gov/search/concepts/C1214313579-AU_AADC.umm_json	The lidar profiles density, temperature, wind velocity and aerosol loading from the lower troposphere to the upper mesosphere, depending on operating mode.  Two main measurement techniques are employed. Firstly, traditional Rayleigh backscatter analysis yields temperature profiles above the top of the stratospheric aerosol layer (greater than about 27km altitude). The temperatures are obtained from lidar-derived density profiles, calibrated with in-situ radiosonde data below 40km altitude, using the standard hydrostatically-constrained perfect gas law model. When available, hydroxyl-layer temperatures obtained locally by a Czerny-Turner spectrograph are used as an upper boundary condition on the temperature retrieval algorithm. Rayleigh backscatter can be detected from altitudes as high as 100km, although useful temperatures are normally limited to below 80km. Observations of rotational-vibrational Raman backscatter from molecular oxygen or nitrogen are used to extend the temperature profiles into the lower stratosphere and upper troposphere. Profiles of aerosol-loading are derived from standard scattering-ratio analysis, allowing identification of clouds in the upper troposphere, stratosphere (Polar Stratospheric Clouds) and mesosphere (Polar Mesospheric Clouds).  Secondly, spectral scans of laser backscatter are obtained with a high-resolution Fabry-Perot spectrometer. These are used to infer the line-of-sight wind speed and temperature by using the Doppler effect. Observations along 'cardinal point' lines-of-sight provide information on wind direction. In general, Doppler measurements are restricted to altitudes below about 70km based on signal detection considerations. Some information on aerosol loading is obtained from analysis of the spectral properties of the backscatter.  The lidar is capable of both day and night measurements covering a large altitude range, and in so doing will provide information for the study of climate change and a range of atmospheric phenomena on a variety of spatial and temporal scales.    Taken from the 2008-2009 Progress Report: Progress against objectives: At Davis, lidar measurements of temperature and aerosol properties were acquired for the troposphere, stratosphere and mesosphere. Additionally, ozone data were acquired for the troposphere and lower stratosphere.  Ongoing analyses of these data is providing new information on the composition, dynamics and climate of the polar atmosphere. During the reporting period, continued progress was achieved in international collaborative studies of Polar Stratospheric Cloud microphysics as part of the International Polar Year, and measurements of Polar Mesospheric Clouds for the Aeronomy of Ice in the Mesosphere (AIM) satellite mission. Both of these activities contribute to all 4 goals of the project.   Taken from the 2009-2010 Progress Report: Progress against objectives: New data were obtained for the study of the long-term climate in the Antarctic middle atmosphere (5-95km altitude), and atmospheric phenomena under extreme physical conditions. The highlights were: (1) Detailed measurements of ice clouds in the summer mesopause region for validation of climate models. (2) Further measurements of the properties and dynamics of Polar Stratospheric Clouds for research aimed at improving projections of the recovery of the Ozone Hole. (3) Initial measurements for a new study of the interactions between the troposphere and stratosphere which is aimed at improved knowledge of climate processes in the tropopause region.	proprietary
-lidar_forest_myotis-myotis_1.0	LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815310-ENVIDAT.umm_json	Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.	proprietary
 lima_Not provided	Landsat Image Mosaic of Antarctica (LIMA)	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567805-USGS_LTA.umm_json	A team of scientists from the U.S. Geological Survey, the British Antarctic Survey, and the National Aeronautics and Space Administration, with funding from the National Science Foundation, created LIMA in support of the International Polar Year (IPY; 2007–08). 	proprietary
-linked-discharge-bedload-transport-and-bedrock-erosion-data-set_1.0	Linked water discharge, bedload transport and bedrock erosion data set in 1minute resolution	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.70875, 47.0449764, 8.70875, 47.0449764	https://cmr.earthdata.nasa.gov/search/concepts/C2789815360-ENVIDAT.umm_json	This data set includes synchronized and independently measured water discharge, bedload transport and at-a-point bedrock erosion data in 1 minute resolution and over more than 1.5 years from the Erlenbach stream hydrological observatory, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. These measurements are of high accuracy, which have been assessed in Beer, A.R. et al. 2015. Earth Surf. Proc., 40, 530-541. doi: 10.1002/esp.3652. For the artificial bedrock (a slab of weak concrete, fixed flush with the streambed) 6 additional consecutive spatial elevation data sets of 1 mm resolution have been surveyed that allow the local continuous erosion measurements to be extended to the patch scale.    This unique data set has been used to validate and calibrate bedrock erosion models for the process to intermediate scales of time (and space), whose performance then was assessed over extended time (up to bicentennial floods), based on available longer data sets of linked discharge and bedload transport (see related datasets).	proprietary
 lipimpacts_2	Lightning Instrument Package (LIP) IMPACTS V2	GHRC_DAAC STAC Catalog	2020-01-15	2023-03-02	-123.62, 26.91, -64.89, 48.66	https://cmr.earthdata.nasa.gov/search/concepts/C2008982738-GHRC_DAAC.umm_json	The Lightning Instrument Package (LIP) IMPACTS dataset consists of electrical field measurements of lightning and navigation data collected by the Lightning Instrument Package (LIP) flown onboard the NASA ER-2 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast (2020-2023). IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The V2 LIP IMPACTS data have been further filtered to remove field mill offsets that were identified in the prior V1 data. These data are available from January 15 through February 28, 2022 in ASCII format. 	proprietary
 lislipG_4	Lightning Imaging Sensor (LIS) on TRMM Backgrounds V4	GHRC_DAAC STAC Catalog	1998-01-01	2015-04-08	-180, -40, 180, 40	https://cmr.earthdata.nasa.gov/search/concepts/C1995583255-GHRC_DAAC.umm_json	The Lightning Imaging Sensor (LIS) Backgrounds was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. These data are available in both HDF-4 and netCDF-4 formats.	proprietary
 lislip_4	Lightning Imaging Sensor (LIS) on TRMM Science Data V4	GHRC_DAAC STAC Catalog	1998-01-01	2015-04-08	-180, -40, 180, 40	https://cmr.earthdata.nasa.gov/search/concepts/C1983762329-GHRC_DAAC.umm_json	The Lightning Imaging Sensor (LIS) Science Data was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. These data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format. 	proprietary
@@ -18143,7 +17658,6 @@ lisvhrdc_1	LIS 0.1 DEGREE VERY HIGH RESOLUTION GRIDDED LIGHTNING DIURNAL CLIMATO
 lisvhrfc_1	LIS 0.1 DEGREE VERY HIGH RESOLUTION GRIDDED LIGHTNING FULL CLIMATOLOGY (VHRFC) V1	GHRC_DAAC STAC Catalog	1998-01-01	2013-12-31	-180, -38, 180, 38	https://cmr.earthdata.nasa.gov/search/concepts/C1979883245-GHRC_DAAC.umm_json	The LIS 0.1 Degree Very High Resolution Gridded Lightning Full Climatology (VHRFC) dataset consists of gridded full climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.	proprietary
 lisvhrmc_1	LIS 0.1 DEGREE VERY HIGH RESOLUTION GRIDDED LIGHTNING MONTHLY CLIMATOLOGY (VHRMC) V1	GHRC_DAAC STAC Catalog	1998-01-01	2013-12-31	-180, -38, 180, 38	https://cmr.earthdata.nasa.gov/search/concepts/C1979883359-GHRC_DAAC.umm_json	The LIS 0.1 Degree Very High Resolution Gridded Lightning Monthly Climatology (VHRMC) dataset consists of gridded monthly climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.	proprietary
 lisvhrsc_1	LIS 0.1 DEGREE VERY HIGH RESOLUTION GRIDDED LIGHTNING SEASONAL CLIMATOLOGY (VHRSC) V1	GHRC_DAAC STAC Catalog	1998-01-01	2013-12-31	-180, -38, 180, 38	https://cmr.earthdata.nasa.gov/search/concepts/C1979883491-GHRC_DAAC.umm_json	The LIS 0.1 Degree Very High Resolution Gridded Lightning Seasonal Climatology (VHRSC) dataset consists of gridded seasonal climatologies of total lightning flash rates seen by the Lightning Imaging Sensor (LIS) from January 1, 1998 through December 31, 2013. LIS is an instrument on the Tropical Rainfall Measurement Mission satellite (TRMM) used to detect the distribution and variability of total lightning occurring in the Earth's tropical and subtropical regions. This information can be used for severe storm detection and analysis, and also for lightning-atmosphere interaction studies. The gridded climatologies include annual mean flash rate, mean diurnal cycle of flash rate with 24 hour resolution, and mean annual cycle of flash rate with daily, monthly, or seasonal resolution. All datasets are in 0.1 degree spatial resolution. The mean annual cycle of flash rate datasets (i.e., daily, monthly or seasonal) have both 49-day and 1 degree boxcar moving averages to remove diurnal cycle and smooth regions with low flash rate, making the results more robust.	proprietary
-literature-data-of-sound-speed-in-snow_1.0	Literature data of sound speed in snow	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815412-ENVIDAT.umm_json	This dataset contains literature data for snow density and frequency dependency of speed of sound waves in snow. The data were either available as tabular data in the original publications or were digitized from plots contained in the original publications. The data were originally collected and used for first figure in Capelli et al. (2016) .	proprietary
 litter_decomp_651_1	Effects of Elevated Carbon Dioxide on Litter Chemistry and Decomposition	ORNL_CLOUD STAC Catalog	1993-01-01	2000-11-21	-180, -47.31, 179.41, 76.63	https://cmr.earthdata.nasa.gov/search/concepts/C2761762163-ORNL_CLOUD.umm_json	The results of published and unpublished experiments investigating the impacts of elevated carbon dioxide on the chemistry (nitrogen and lignin concentration) of leaf litter and the decomposition of plant tissues are assembled in a format appropriate for statistical meta-analysis of the effect of carbon dioxide.	proprietary
 lohrac_2.3.2015	LIS/OTD 0.5 Degree High Resolution Annual Climatology (HRAC) V2.3.2015	GHRC_DAAC STAC Catalog	1995-05-04	2014-12-31	-179.75, -89.75, 179.75, 89.75	https://cmr.earthdata.nasa.gov/search/concepts/C1995863067-GHRC_DAAC.umm_json	The LIS/OTD 0.5 Degree High Resolution Annual Climatology (HRAC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The HRAC dataset includes annual flash rate climatology data on a 0.5 degree grid in HDF and netCDF-4 format.	proprietary
 lohrfc_2.3.2015	LIS/OTD 0.5 Degree High Resolution Full Climatology (HRFC) V2.3.2015	GHRC_DAAC STAC Catalog	1995-05-04	2014-12-31	-179.75, -89.75, 179.75, 89.75	https://cmr.earthdata.nasa.gov/search/concepts/C1995863244-GHRC_DAAC.umm_json	The LIS/OTD 0.5 Degree High Resolution Full Climatology (HRFC)  contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite.The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The HRFC dataset include flash rate climatology data including raw and scaled flash on a 0.5 degree grid in HDF and netCDF-4 format.	proprietary
@@ -18155,10 +17669,7 @@ lolrdc_2.3.2015	LIS/OTD 2.5 Degree Low Resolution Diurnal Climatology (LRDC) V2.
 lolrfc_2.3.2015	LIS/OTD 2.5 Degree Low Resolution Full Climatology (LRFC) V2.3.2015	GHRC_DAAC STAC Catalog	1995-05-04	2014-12-31	-178.75, -88.75, 178.75, 88.75	https://cmr.earthdata.nasa.gov/search/concepts/C1995864215-GHRC_DAAC.umm_json	The LIS/OTD 2.5 Degree Low Resolution Full Climatology (LRFC) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRFC dataset include flash rate climatology data including raw and scaled flashes on a 2.5 degree grid in HDF and netCDF-4 format.	proprietary
 lolrmts_2.3.2015	LIS/OTD 2.5 Degree Low Resolution Monthly Climatology Time Series (LRMTS) V2.3.2015	GHRC_DAAC STAC Catalog	1995-05-04	2015-04-08	-178.75, -88.75, 178.75, 88.75	https://cmr.earthdata.nasa.gov/search/concepts/C1995865015-GHRC_DAAC.umm_json	The LIS/OTD 2.5 Degree Low Resolution Monthly Climatology Time Series (LRMTS) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRMTS dataset include monthly flash rate time series data in MP4 format.	proprietary
 lolrts_2.3.2015	LIS/OTD 2.5 Degree Low Resolution Time Series (LRTS) V2.3.2015	GHRC_DAAC STAC Catalog	1995-05-04	2015-04-08	-178.75, -88.75, 178.75, 88.75	https://cmr.earthdata.nasa.gov/search/concepts/C1995865470-GHRC_DAAC.umm_json	The LIS/OTD 2.5 Degree Low Resolution Time Series (LRTS) contains a variety of gridded climatologies of total lightning flash rates obtained from two lightning detection sensors - the spaceborne Optical Transient Detector (OTD) on Orbview-1 and the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The long LIS (equatorward of about 38 degree) record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The LRTS dataset include flash rate time series data in MP4 format.	proprietary
-long-term-recovery-of-above-and-belowground-interactions-in-restored-grasslands_1.0	Long-term recovery of above-and belowground interactions in restored grasslands	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815634-ENVIDAT.umm_json	This dataset contains all data, on which the following publication below is based.    __Paper Citation:__    _Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_    __Please cite this paper together with the citation for the datafile.__    Study area and experimental design  The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).    Collection of plants and selected soil biota data  Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.  We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.    Collection of soil physical and chemical properties  We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.	proprietary
 long_tryne_bathy_1	Interpolated bathymetry of Long and Tryne Fjords, Vestfold Hills, Antarctica	AU_AADC STAC Catalog	2000-07-01	2000-11-15	78, -68.5833, 78.5833, -68.4	https://cmr.earthdata.nasa.gov/search/concepts/C1214311181-AU_AADC.umm_json	This GIS dataset is the result of the interpolation of bathymetry from depth measurements made in Long and Tryne Fjords in the Vestfold Hills, Antarctica (see Entry: VH_bathy_99). The Topogrid command within the ArcInfo GIS software, version 8.0.2, was used to do the interpolation. Coastline and spot height (heights above sea level) data, extracted from the Australian Antarctic Data Centre's Vestfold Hills topographic GIS dataset (see Entry: vest_hills_gis), was also used as input data to optimise the interpolation close to the coastline. See related URLs for a map showing the interpolated bathymetry.	proprietary
-longterm-hydrological-observatory-alptal-central-switzerland_2.0	Longterm hydrological observatory Alptal (central Switzerland)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	8.7052166, 47.0466744, 8.7052166, 47.0466744	https://cmr.earthdata.nasa.gov/search/concepts/C2789815465-ENVIDAT.umm_json	This data set includes 54 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in sub-daily resolution can be provided on demand.  Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and Lümpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal  .  Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.   Meteorological variables have been measured on a meadow (Erlenhöhe) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and Lümpenenbach catchments).  Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenhöhe, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in Stähli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.    Further information on the methods and sensors can be found at https://www.wsl.ch/alptal  .    A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. A second update of the data set (with data from 2020 to 2022) was uploaded in March 2023.	proprietary
-lsa_forest_snow_1.0	Wintertime UAV-based land surface albedo data over forested environments	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.8704777, 46.8432022, 9.8776875, 46.8462549	https://cmr.earthdata.nasa.gov/search/concepts/C2789815873-ENVIDAT.umm_json	"This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper ""Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements"", by Malle et al. (2021, under review). This publication must be cited when using the data.    Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankylä). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation."	proprietary
 lsatmssd_435_1	BOREAS Landsat MSS Imagery: Digital Counts	ORNL_CLOUD STAC Catalog	1972-08-21	1988-09-05	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2928014125-ORNL_CLOUD.umm_json	A set of MSS images from Landsat satellites 1, 2, 4 and 5 covering the dates of 21-Aug-1972 to 05-Sep-1988.  	proprietary
 ltm_ii3a_280_1	BOREAS Landsat TM Level-3a Imagery: Scaled At-Sensor Radiance in BSQ Format	ORNL_CLOUD STAC Catalog	1984-06-22	1996-07-30	-106.23, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2927613321-ORNL_CLOUD.umm_json	For BOREAS, the level-3A Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas.  This information includes radiant energy, detailed land cover, and biophysical parameter maps such as fPAR and LAI. Geographically, the level-3a images cover the BOREAS NSA and SSA.  Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996.  The images are available in binary, image-format files.	proprietary
 ltm_ii3b_425_1	BOREAS Landsat TM Level-3b Imagery: At-Sensor Radiance in BSQ Format	ORNL_CLOUD STAC Catalog	1984-06-22	1996-07-30	-106.32, 53.42, -97.34, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2927746132-ORNL_CLOUD.umm_json	For BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas.  This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI.  Geographically, the level-3b images cover the BOREAS NSA and SSA.  Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996.	proprietary
@@ -18168,45 +17679,7 @@ ltmmaxln_429_1	BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image o
 ltmmaxls_430_1	BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the SSA	ORNL_CLOUD STAC Catalog	1990-08-06	1990-08-06	-106.51, 53.33, -104.28, 54.44	https://cmr.earthdata.nasa.gov/search/concepts/C2927998856-ORNL_CLOUD.umm_json	A Landsat-5 TM image from 06-Aug-1990 was used to derive this classification, the objective of which is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A standard supervised maximum likelihood classification approach was used to produce this classification.	proprietary
 ltmphysn_431_1	BOREAS TE-18 Landsat TM Physical Classification Image of the NSA	ORNL_CLOUD STAC Catalog	1995-06-21	1995-06-21	-99.2, 55.38, -97.24, 56.26	https://cmr.earthdata.nasa.gov/search/concepts/C2928000847-ORNL_CLOUD.umm_json	The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training  data to classify the image into the different land cover classes.	proprietary
 ltmphyss_432_1	BOREAS TE-18 Landsat TM Physical Classification Image of the SSA	ORNL_CLOUD STAC Catalog	1994-09-02	1994-09-02	-106.52, 53.31, -104.19, 54.44	https://cmr.earthdata.nasa.gov/search/concepts/C2928001982-ORNL_CLOUD.umm_json	The objective of this classification is to provide BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes.	proprietary
-luszoning_1.0	LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	-4.4934082, 38.7600804, 2.7355957, 42.2504783	https://cmr.earthdata.nasa.gov/search/concepts/C2789816049-ENVIDAT.umm_json	"Table of Content: 1. General context of the data set ""LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.  ###1. __General context of the data set ""LUSzoning"".__   The data set ""LUSzoning"" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.   ###2.	__Background and aims of the study using the data set “LUSzoning”.__   As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.   Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.  ###3.	 __The data set “LUSzoning”.__   The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed."	proprietary
 lutzow_holm_bay_bathy_1	Bathymetry of Lutzow-Holm Bay digitised by NIPR from bathymetric chart of Lutzow-Holm Bay	AU_AADC STAC Catalog	2002-01-01	2002-12-31	37.45, -69.25, 37.55, -69.166	https://cmr.earthdata.nasa.gov/search/concepts/C1214313601-AU_AADC.umm_json	The soundings were digitized from bathymetric chart: Bathymetry of Lutzow-Holm Bukta (Lutzow-Holm Bay) by the Japanese, National Institute of Polar Research (NIPR) from Special Map Series of National Institute of Polar Research No. 4b, 2002 - map number 12852 in the SCAR map catalogue.  These data have been created by the Japanese, but as such no metadata record for the data exists in the Japanese portal of the Antarctic Master Directory.  Australian users of these data should use this metadata record (providing credit to the Japanese), until a Japanese version has been created.	proprietary
-lwf-alptal-long-term-research-site_1.0	LWF Alptal long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.713054, 47.04872, 8.713054, 47.04872	https://cmr.earthdata.nasa.gov/search/concepts/C2789816190-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg ""LWF Alptal"")    LWF plot Alptal    - Community: Alpthal / canton SZ  - Date of installation: 31 May 1995  - Size of the plot: 0.6 ha  - Altitude: 1149-1170 m  - Mean slope: 23%  - Geology (in German): Nordpenninikum; obere Kreide-unteres Eozän, Wägitaler Flysch  - Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols  - Woodland association after EK72: 49: Equiseto-Abietetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm  - Number of trees BHD >= 12 cm (2011): 321  - Maximum tree age: Picea abies 180-230 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html"	proprietary
-lwf-beatenberg-long-term-research-site_1.0	LWF Beatenberg long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.7623374, 46.7003438, 7.7623374, 46.7003438	https://cmr.earthdata.nasa.gov/search/concepts/C2789816263-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/7310b935-757f-4f27-b202-9f433c9882ab/resource/555b1a19-aff3-40be-9d6d-fea9967d5691/download/lwf_beatenberg.jpg ""LWF Beatenberg"")    LWF plot Beatenberg    - Community: Beatenberg / canton BE  - Date of installation: 25 September 1996  - Size of the plot: 2 ha  - Altitude: 1490-1532 m  - Mean slope: 66%  - Geology (in German): Helvetikum, Tertiär, Eozän; Hohgantsandstein  - Provisional soil type (WSL) : Gleyic Podzols  - Woodland association after EK72: 57: Sphagno-Piceetum calamagrostietosum villosae  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 46.4 cm  - Number of trees BHD >= 12 cm (2011): 851  - Maximum tree age: Picea abies 190-210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/beatenberg.html"	proprietary
-lwf-bettlachstock-long-term-research-site_1.0	LWF Bettlachstock long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.4166536, 47.2251551, 7.4166536, 47.2251551	https://cmr.earthdata.nasa.gov/search/concepts/C2789816308-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/8f67193b-12cf-4871-9a9e-750b816e9d10/resource/b05db334-5bf6-42d6-a985-a5253366259d/download/lwf_bettlachstock.jpg ""LWF Bettlachstock"")    LWF Plot Bettlachstock    - Community: Bettlachstock / canton SO  - Date of installation: 6 June 1995  - Size of the plot: 1.28 ha  - Altitude: 1101-1196 m  - Mean slope: 66%  - Geology (in German): Kettenjura; Jura: Dogger, oberer Hauptrogenstein  - Soil types (WSL) : Rendzic Leptosols; Calcaric Cambisols  - Woodland association after EK72: 13 h: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 49.5 cm  - Number of trees BHD >= 12 cm (2011): 632  - Maximum tree age: Fagus sylvatica 170-190 yr - Picea abies 200 yr - Fraxinus excelsior 170 yr - Ulmus glabra 160 yr - Abies alba 190 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/bettlachstock.html"	proprietary
-lwf-celerina-long-term-research-site_1.0	LWF Celerina long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8888024, 46.4921451, 9.8888024, 46.4921451	https://cmr.earthdata.nasa.gov/search/concepts/C2789816326-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/0c244fe7-886a-4a1c-add5-5f706e995a29/resource/28e97caa-65fc-4396-8bca-860721eddfa2/download/lwf_celerina.jpg ""LWF Celerina"")    LWF Plot Celerina    - Community: Celerina / canton GR  - Date of installation: 3 July 1996  - Size of the plot: 2 ha  - Altitude (m): 1846-1896  - Mean slope: 34%  - Geology (in German): Untergrund: ostalpin; prätriadische Tiefengesteine - Oberfläche: Quartär; karbonatfreie Moräne  - Soil types (WSL): n.d.  - Woodland association after EK72: 59: Larici-Pinetum cembrae  - Main tree species: Pinus cembra  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 48.6 cm  - Number of trees BHD >= 12 cm (2011): 469  - Maximum tree age: Pinus cembra uneven-aged - 210-250 years    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/celerina.html"	proprietary
-lwf-chironico-long-term-research-site_1.0	LWF Chironico long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.812172, 46.4468172, 8.812172, 46.4468172	https://cmr.earthdata.nasa.gov/search/concepts/C2789816346-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg ""LWF Chironico"")    LWF Plot Chironico    - Community: Chironico / canton TI  - Date of installation: 29 August 1995  - Size of the plot: 2 ha  - Altitude: 1342-1387 m  - Mean slope: 35%  - Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfläche: Quartär; karbonatfreie Moräne, Hängeschutt  - Provisional soil type (WSL) : Distric Cambisol  - Woodland association after EK72: 47: Calamagrostio villosae-Abietetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm  - Number of trees BHD >= 12 cm (2011): 750  - Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/chironico.html"	proprietary
-lwf-isone-long-term-research-site_1.0	LWF Isone long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.0080555, 46.1248982, 9.0080555, 46.1248982	https://cmr.earthdata.nasa.gov/search/concepts/C2789816400-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg ""LWF Isone"")    LWF Plot Isone    - Community: Isone / canton TI  - Date of installation: 5 September 1995  - Size of the plot: 2 ha  - Altitude (m): 1181-1259  - Mean slope: 58%  - Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; Moräne, Hängeschutt-.  - Provisional soil type (WSL) : Humic Cambisol  - Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum  - Main tree species: Fagus sylvatica  - Management system: former coppice  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm  - Number of trees BHD >= 12 cm (2011): 1254  - Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html"	proprietary
-lwf-jussy-long-term-research-site_1.0	LWF Jussy long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.2908547, 46.2298528, 6.2908547, 46.2298528	https://cmr.earthdata.nasa.gov/search/concepts/C2789816503-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/465d852d-af24-415f-9db2-48fe31d6dc20/resource/a39a0e35-efdf-4bef-bab1-4b0532442b34/download/lwf_jussy.jpg ""LWF Jussy"")    LWF Plot Jussy    - Community: Jussy / canton GE  - Date of installation: 31 May 1995  - Size of the plot: 1.99 ha  - Altitude: 496-506 m  - Mean slope: 3%  - Geology (in German): Quartär; tonreiche würmeiszeitliche Grundmoräne  - Soil types (WSL) : Stagnic Luvisols  - Woodland association after EK72: 35: Galio silvatici-Carpinetum  - Main tree species: Quercus species  - Management system: former coppices w. standards  - Silvicultural system: unmanaged / group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 36.6 cm  - Number of trees BHD >= 12 cm (2011): 1278  - Maximum tree age: Carpinus betulus 60 yr - Populus tremula 60 yr - Quercus petrea 90 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/jussy.html"	proprietary
-lwf-lageren-long-term-research-site_1.0	LWF Lägeren long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.3645212, 47.4783603, 8.3645212, 47.4783603	https://cmr.earthdata.nasa.gov/search/concepts/C3226082992-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg ""LWF Lägeren"")    This research site is located on the southern slope of the Lägern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks:     * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))  * [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network  * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html))  * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)    The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)).    LWF Plot Lägeren    - Community: Wettingen / canton AG  - Date of installation: 1.05.2012  - Size of the plot: 1.34 ha  - Altitude: 643 - 718 m  - Mean slope: 37 %  - Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt  - Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina  - Woodland association after Ellenberg and Klötzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum  - Main tree species: fagus sylvatica  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm  - Number of trees BHD >= 12 cm (2011): 503  - Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years"	proprietary
-lwf-lantsch-long-term-research-site_1.0	LWF Lantsch long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.5646678, 46.6980544, 9.5646678, 46.6980544	https://cmr.earthdata.nasa.gov/search/concepts/C2789815332-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/9e5d6c82-26ac-4818-9458-1ac7c8574bb0/resource/c26169dc-db58-4e69-bc74-9513dbf7bccc/download/lwf_lantsch.jpg ""LWF Lantsch"")    LWF Plot Lantsch    - Community: Lantsch / canton GR  - Date of installation: 15 September 1997  - Size of the plot: n.d.  - Altitude: 1458-1490 m  - Mean slope: 16%  - Geology (in German): Ostalpin. Gehängeschutt aus mesozoischen Schiefern, Dolomiten und Kalken  - Soil types (WSL) : n.d.  - Woodland association after EK72: 65: Erico-Pinetum silvestris  - Main tree species: Picea abies  - Management system: high forest  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 40.1 cm  - Number of trees BHD >= 12 cm (2011): 709  - Maximum tree age: n.d.    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lantsch.html"	proprietary
-lwf-lausanne-long-term-research-site_1.0	LWF Lausanne long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.6580421, 46.5837664, 6.6580421, 46.5837664	https://cmr.earthdata.nasa.gov/search/concepts/C2789815359-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/9f35f697-a226-4719-aefd-b8c96fe5ae7c/resource/d6a48c12-62e6-4c04-9f63-a84d5964bc55/download/lwf_lausanne.jpg ""LWF Lausanne"")    LWF Plot Lausanne    - Community: Lausanne / canton VD  - Date of installation: 5 September 1994  - Size of the plot: 2 ha  - Altitude: 800-814 m  - Mean slope: 7%  - Geology (in German): Untergrund: Tertiär, Miozän, Burdigalien, obere Meeresmolasse; Sandstein - Oberfläche: Quartär, Würm; würmeiszeitliche Moräne  - Soil types (WSL) : Distric Cambisols  - Woodland association after EK72: 8: Milio-Fagetum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 59.9 cm  - Number of trees BHD >= 12 cm (2011): 650  - Maximum tree age: Abies alba 160-170 yr - Picea abies 160-170 yr - Fagus sylvatica 160-170 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lausanne.html"	proprietary
-lwf-lens-long-term-research-site_1.0	LWF Lens long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.4359392, 46.2685558, 7.4359392, 46.2685558	https://cmr.earthdata.nasa.gov/search/concepts/C2789815410-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/87d3ea5a-8d50-446b-9c33-cbe56057f7d3/resource/bf459948-da5a-4b05-9372-83ca5454aeca/download/lwf_lens.jpg ""LWF Lens"")    LWF Plot Lens    - Community: Lens / canton VS  - Date of installation: 15 March 1996  - Size of the plot: 2 ha  - Altitude: 1033-1093 m  - Mean slope: 75%  - Geology (in German): Untergrund: Penninikum, Ferret-Zone, Trias; sandiger Kalkstein - Oberfläche: Hängeschutt  - Provisional soil type (WSL): Calcaric Cambisol  - Woodland association after EK72: +- 64: Cytiso-Pinetum silvestris  - Main tree species: Pinus sylvestris  - Management system: high forest  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 31.8 cm  - Number of trees BHD >= 12 cm (2011): 2304  - Maximum tree age:150-170 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lens.html"	proprietary
-lwf-nationalpark-long-term-research-site_1.0	LWF Nationalpark long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	10.2300874, 46.662501, 10.2300874, 46.662501	https://cmr.earthdata.nasa.gov/search/concepts/C2789816200-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/4a0ff376-83d2-4ae4-bf2e-c361eb050778/resource/51e98f2d-5524-4ea6-babb-9df0b9aba8f1/download/lwf_nationalpark.jpg ""LWF Nationalpark"")    LWF Plot Nationalpark    - Community: Zernez / canton GR  - Date of installation: 10 October 1995  - Size of the plot: 2 ha  - Altitude: 1890-1907 m  - Mean slope: 11%  - Geology (in German): Nacheiszeitlicher Schwemmfächer; kalkhaltige Moräne, Dolomite, Kalke, Tonschiefer, Rauhwacken  - Provisional soil types (WSL): Rendzic Leptosol  - Woodland association after EK72: 67: Erico-Pinetum montanae  - Main tree species: Pinus mugo  - Management system: high forest  - Silvicultural system: forest reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 23.7 cm  - Number of trees BHD >= 12 cm (2011): 2450  - Maximum tree age: Pinus mugo 210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/nationalpark.html"	proprietary
-lwf-neunkirch-long-term-research-site_1.0	LWF Neunkirch long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.535683, 47.6837031, 8.535683, 47.6837031	https://cmr.earthdata.nasa.gov/search/concepts/C2789816296-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg ""LWF Neunkirch"")      LWF Plot Neunkirch    - Community: Neunkirch / canton SH  - Date of installation: 14 July 1995  - Size of the plot: 2 ha  - Altitude (m): 554-609  - Mean slope: 58%  - Geology (in German): Tafeljura, oberer Malmkalk; Malmhängeschutt  - Soil types (WSL) : Rendzic Leptosols  - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: former coppices w. standards  - Silvicultural system: reserve  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm  - Number of trees BHD >= 12 cm (2011): 442  - Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html"	proprietary
-lwf-novaggio-long-term-research-site_1.0	LWF Novaggio long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.8341613, 46.0226119, 8.8341613, 46.0226119	https://cmr.earthdata.nasa.gov/search/concepts/C2789816319-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/e87358e9-4beb-487e-af68-66633e9cfc96/resource/91bd053a-61c4-4b20-b0d3-a572c983e647/download/lwf_novaggio.jpg ""LWF Novaggio"")    LWF Plot Novaggio    - Community: Novaggio / canton TI  - Date of installation: 8.3.95  - Size of the plot: 1.5 ha  - Altitude (m): 902-997  - Mean slope: 68%  - Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge; Orthogneis, schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; karbonatfreie würmeiszeitliche Moräne  - Provisional soil type (WSL): Kryptopodzole  - Woodland association after EK72: 42: Phyteumo betonicifoliae-Quercetum castanosum  - Main tree species: Quercus cerris  - Management system: former coppice  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 27.0 cm  - Number of trees BHD >= 12 cm (2011): 1130  - Maximum tree age: Castanea sativa 90 yr- Betula pendula 70 yr - Quercus cerris 70 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/novaggio.html"	proprietary
-lwf-othmarsingen-long-term-research-site_1.0	LWF Othmarsingen long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.2267566, 47.3995354, 8.2267566, 47.3995354	https://cmr.earthdata.nasa.gov/search/concepts/C2789816335-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/ebb37e73-1280-4a06-81d8-e18bc5d9c1cf/resource/64291bfd-37d4-4f88-b505-81fc85a109c2/download/lwf_othmarsingen.jpg ""LWF Othmarsingen"")    LWF Plot Othmarsingen    - Community: Othmarsingen / canton AG  - Date of installation: 9 September 1994  - Size of the plot: 1 ha  - Altitude (m): 467-500  - Mean slope: 27%  - Soil types (WSL): Stagnic Luvisols, Haplic Luvisols  - Woodland association after EK72: 7: Galio odorati-Fagetum typicum  - Main tree species: Fagus sylvatica  - Management system: former coppices w. standards  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 62.8 cm  - Number of trees BHD >= 12 cm (2011): 167  - Maximum tree age: Fagus sylvatica 120-140 yr - Tilia sp. 120-140 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/othmarsingen.html"	proprietary
-lwf-pfynwald-long-term-experimental-irrigation-site_1.0	LWF Pfynwald long-term experimental irrigation site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.6121082, 46.3027884, 7.6121082, 46.3027884	https://cmr.earthdata.nasa.gov/search/concepts/C2789816348-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg ""LWF experimental irrigation site Pfynwald"")      As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46° 18' N, 7° 36' E, 615 m ASL) offers the best conditions for such measurements.  In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest.  The average temperature here is 9.2°C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions."	proprietary
-lwf-schanis-long-term-research-site_1.0	LWF Schänis long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.0670726, 47.1650464, 9.0670726, 47.1650464	https://cmr.earthdata.nasa.gov/search/concepts/C2789816393-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg ""LWF Schänis"")    LWF Plot Schänis    - Community: Schänis / canton SG  - Date of installation: 17 September 1997  - Size of the plot: 2 ha  - Altitude: 693-773 m  - Mean slope: 60%  - Geology (in German): Tertiär. Subalpine Molasse, Oligocaen, Chattien, Kalknagelfluh  - Soil types (WSL) : n.d.  - Woodland association after EK72: 13: Cardamino-Fagetum tilietosum  - Main tree species: Fagus sylvatica  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 55.8 cm  - Number of trees BHD >= 12 cm (2011): 611  - Maximum tree age: Abies alba130-150 yr - Fraxinus excelsior 130-150 yr - Fagus sylvatica 130-150 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/schaenis.html"	proprietary
-lwf-seehornwald-davos-long-term-research-site_1.0	LWF Seehornwald Davos long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8552112, 46.8153458, 9.8552112, 46.8153458	https://cmr.earthdata.nasa.gov/search/concepts/C3226082636-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/801cdd7e-f5b0-4998-bb8a-bd6d2ae8baa2/resource/f2ef5505-e0ea-493d-8b86-4f27dd556da8/download/lwf_davos.jpg ""LWF Davos"")    This research site is located on the Seehorn mountain near Davos within a managed subalpine coniferous forest in the Swiss Alps. Seehronwald Davos site is dedicated to forest ecosystem research with current projects focusing on topics of climate change, ecosystem carbon balance, ecophysiology, vegetation and soil sciences. The site belongs to one of the best equipped long-term forest ecology research sites of the world. Time series of climate variables, ecosystem gas exchange (eddy covariance), tree physiology records (sap flow, stem radius changes), and air pollution data cover the history of this site over more than 20 years. Records of local climate variables started in 1876. Since 2013 the site is part of [ICOS](https://www.icos-cp.eu), which awarded the infrastructure the CLASS 1 label on 21 November 2019. The site is part of the following national and international networks and encourages further synergistic collaborations with scientists from all over the world:     * National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))  * ICOS Switzerland ([ICOS-CH](https://www.icos-switzerland.ch/davos))  * [TreeNet](https://treenet.info/switzerland/davos): The biological drought and growth indicator network  * Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/davos.html))  * [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav)  * Ecosystem Research ([ExpeER](http://www.expeeronline.eu/43-expeer-ta-sites/131-davos-seehornwald-switzerland.html))  * Long Term Ecological Research ([LTER](https://www.lter-europe.net))  * [ICP Forests](http://icp-forests.net): the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests    The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the Swiss Federal Institute of Technology Zurich ([ETHZ](https://www.gl.ethz.ch)), and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)) in Birmensdorf and Davos. The infrastructure is provided by the Federal Office of Environment ([FOEN](https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html)). All partners are grateful to forest owners and to the forestry service of the community of Davos for their continuous support.     LWF Plot Davos    - Community: Davos / canton GR  - Date of installation: 15.06.2006  - Size of the plot: 0.6 ha  - Altitude: : 1635-1665  - Geology (in German): Untergrund:  - Oberfläche:   - Provisional soil type (WSL):   - Woodland association after EK72: 58: Larici-Piceetum  - Main tree species: Picea abies  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 47.0 cm  - Number of trees BHD >= 12 cm (2006): 498  - Maximum tree age: Picea abies: 200 - 390 yr"	proprietary
-lwf-tea-bag-sites_1.0	LWF-Tea bag sites	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	7.416653, 46.022611, 9.067072, 47.361944	https://cmr.earthdata.nasa.gov/search/concepts/C2789816602-ENVIDAT.umm_json	Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.  The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.  The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.	proprietary
-lwf-visp-long-term-research-site_1.0	LWF Visp long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.8583245, 46.2968789, 7.8583245, 46.2968789	https://cmr.earthdata.nasa.gov/search/concepts/C2789816713-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg ""LWF Visp"")    LWF Plot Visp    - Community: Visp / canton VS  - Date of installation: 13 March 1996  - Size of the plot: 2 ha  - Altitude: 657-733 m  - Mean slope: 80%  - Geology (in German): Penninikum, Jura, Bündnerschiefer; Kalkphyllite, Hängeschutt  - Provisional soil type (WSL): Calcaric Cambisol  - Woodland association after EK72: =~= 38: Arabidi turritae-Quercetum pubescentis  - Main tree species: Pinus sylvestris  - Management system: high forest  - Silvicultural system: unmanaged  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm  - Number of trees BHD >= 12 cm (2011): 650  - Maximum tree age: Pinus sylvestris uneven-aged 40-80 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/visp.html"	proprietary
-lwf-vordemwald-long-term-research-site_1.0	LWF Vordemwald long-term research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.8867633, 47.2740627, 7.8867633, 47.2740627	https://cmr.earthdata.nasa.gov/search/concepts/C2789816855-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/bffc768d-e3b2-41b2-9b5f-3c1ecbc3ce74/resource/e129ebc8-ef61-4e19-8e89-e18d591ed8c5/download/lwf_vordemwald.jpg ""LWF Vordemwald"")    LWF Plot Vordemwald    - Community: Vordemwald / canton AG  - Date of installation: 18 August 1995  - Size of the plot: 2 ha  - Altitude: 473-487 m  - Mean slope: 14%  - Geology (in German): Untergrund: Oligozän, Aquitanien, untere Süsswassermolasse, bunte Mergel - Oberfläche: Rissgrundmoräne  - Soil types (WSL): Distric Gleysols  - Woodland association after EK72: 46: Bazzanio-Abietetum  - Main tree species: Abies alba  - Management system: high forest  - Silvicultural system: group selection  - Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm  - Number of trees BHD >= 12 cm (2011): 1084  - Maximum tree age: Abies alba 110 yr - Quercus sp. 190-210 yr    More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/vordemwald.html"	proprietary
-lwfmeteo-alpthal_1.0	Alpthal, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.713054, 47.04872, 8.713054, 47.04872	https://cmr.earthdata.nasa.gov/search/concepts/C2789815452-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-beatenberg_1.0	Beatenberg, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.768, 46.7, 7.768, 46.7	https://cmr.earthdata.nasa.gov/search/concepts/C2789815579-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the  Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-bettlachstock_1.0	Bettlachstock, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.415, 47.223, 7.415, 47.223	https://cmr.earthdata.nasa.gov/search/concepts/C2789815913-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-celerina_1.0	Celerina, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.882, 46.499, 9.882, 46.499	https://cmr.earthdata.nasa.gov/search/concepts/C2789816264-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-chironico_1.0	Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.816, 46.444, 8.816, 46.444	https://cmr.earthdata.nasa.gov/search/concepts/C2789816310-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-isone_1.0	Isone, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.007, 46.126, 9.007, 46.126	https://cmr.earthdata.nasa.gov/search/concepts/C2789816327-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-jussy_1.0	Jussy, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.29, 46.23, 6.29, 46.23	https://cmr.earthdata.nasa.gov/search/concepts/C2789816343-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-lausanne_1.0	Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.653, 46.571, 6.653, 46.571	https://cmr.earthdata.nasa.gov/search/concepts/C2789816396-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-lens_1.0	Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.435198, 46.268368, 7.435198, 46.268368	https://cmr.earthdata.nasa.gov/search/concepts/C2789816490-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-nationalpark_1.0	Nationalpark, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	10.236, 46.661, 10.236, 46.661	https://cmr.earthdata.nasa.gov/search/concepts/C2789816612-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-neunkirch_1.0	Neunkirch, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.53, 47.687, 8.53, 47.687	https://cmr.earthdata.nasa.gov/search/concepts/C2789816739-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-novaggio_1.0	Novaggio, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.835, 46.023, 8.835, 46.023	https://cmr.earthdata.nasa.gov/search/concepts/C2789815335-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-othmarsingen_1.0	Othmarsingen, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.225, 47.399, 8.225, 47.399	https://cmr.earthdata.nasa.gov/search/concepts/C2789815361-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-schaenis_1.0	Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.063, 47.159, 9.063, 47.159	https://cmr.earthdata.nasa.gov/search/concepts/C2789815414-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-visp_1.0	Visp, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.858, 46.298, 7.858, 46.298	https://cmr.earthdata.nasa.gov/search/concepts/C2789815495-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
-lwfmeteo-vordemwald_1.0	Vordemwald, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.899, 47.271, 7.899, 47.271	https://cmr.earthdata.nasa.gov/search/concepts/C2789815732-ENVIDAT.umm_json	High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.	proprietary
 mac_isl_sat_1	Macquarie Island georeferenced satellite image negative 1:200 000	AU_AADC STAC Catalog	1991-11-01	1991-11-30	158.76892, -54.78168, 158.96393, -54.47882	https://cmr.earthdata.nasa.gov/search/concepts/C1214313602-AU_AADC.umm_json	Geo-referenced satellite images of Macquarie Island. These images were produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia. The images are at a scale of 1:200 000, and were produced from SPOT3 Pan and XS scenes. It is projected on a Transverse Mercator projection. An interim image map has been produced at a scale of 1:50 000. The details which follow relate to this interim map.	proprietary
 macca_SPOT3_georef_1	Georeferenced satellite image of Macquarie Island	AU_AADC STAC Catalog	1994-12-22	1994-12-22	158.77167, -54.78168, 158.95981, -54.48121	https://cmr.earthdata.nasa.gov/search/concepts/C1214311201-AU_AADC.umm_json	This metadata record describes a georeferenced satellite image of Macquarie Island captured on December 22, 1994 by the French SPOT satellite.  The download file contains the following files:  macquarie_island.bil macquarie_island.hdr Processing_information.txt Readme.txt schema.ini  The image was produced for the Australian Antarctic Division by   AUSLIG Commercial (now known as Geoscience Australia), in Australia. The image was produced from SPOT3 Pan and XS scenes.  It is projected on a UTM, zone 57 projection.	proprietary
 macca_aeronautical_gis_2	Macquarie Island Aeronautical GIS Dataset	AU_AADC STAC Catalog	1997-09-01	2010-03-31	158.77167, -54.7801, 158.97217, -54.47961	https://cmr.earthdata.nasa.gov/search/concepts/C1214311182-AU_AADC.umm_json	Helicopter landing sites on Macquarie Island. This is a point dataset in the Geographical Information System (GIS).  This dataset is a March, 2010 revision.	proprietary
@@ -18227,20 +17700,14 @@ madagascar_diatoms_Not provided	MADAGASCAR National Oceanographic Data Centre -
 madagascar_dinoflagelles_Not provided	MADAGASCAR National Oceanographic Data Centre - Dinoflagellates	CEOS_EXTRA STAC Catalog	2002-12-01	2003-12-31	43.61, -23.38, 43.68, -23.35	https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.umm_json	The Madagascar National Oceanographic Data Centre is attached to the University of Toliara.  Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast.  This dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families.	proprietary
 madagascar_fish_Not provided	MADAGASCAR National Oceanographic Data Centre - Fish	CEOS_EXTRA STAC Catalog	2001-07-29	2001-08-18	43.58, -23.38, 43.58, -23.38	https://cmr.earthdata.nasa.gov/search/concepts/C2232477670-CEOS_EXTRA.umm_json	The Madagascar National Oceanographic Data Centre is attached to the University of Toliara.  Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast.  This dataset has been collected in Toliara Bay, and includes bony fish, cartilagenous fish, mammals and reptiles. It currently consists of 721 records of 49 families.</Summary>	proprietary
 madagascar_invertebrates_Not provided	MADAGASCAR National Oceanographic Data Centre - Invertebrates	CEOS_EXTRA STAC Catalog	2001-07-29	2001-08-18	43.58, -23.38, 43.58, -23.38	https://cmr.earthdata.nasa.gov/search/concepts/C2232477672-CEOS_EXTRA.umm_json	The Madagascar National Oceanographic Data Centre is attached to the University of Toliara.  Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast.  This dataset has been collected in Toliara Bay, and includes mollusks, echinoderms, crustaceans, sponges and annelids. It currently consists of 230 records of 7 phylums.	proprietary
-madcrypto-bryophyte-and-macrolichen-diversity-in-laurel-forests-of-madeira_1.0	MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-17.2883606, 32.6278099, -16.6676331, 32.8726671	https://cmr.earthdata.nasa.gov/search/concepts/C2789815340-ENVIDAT.umm_json	This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).    The dataset was used for the paper   Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.    The excel file contains 5 sheets:  1) Plot information  2) Bryophyte data with species specific information, separated per substrate  3) Macrolichen data with species specific information, separated per substrate  4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.  5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.	proprietary
 magnetic_domec_1977_1	Magnetic Readings Along Pioneerskaya - Dome C Traverses, 1977 and 1978	AU_AADC STAC Catalog	1977-01-01	1978-12-31	90, -75, 125, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214311206-AU_AADC.umm_json	Magnetic readings taken along the Russian traverse from Pioneerskaya to Dome C in 1977 and 1978.  Copies of these documents have been archived in the records store of the Australian Antarctic Division.	proprietary
-manual-measuring-network_1.0	Manual measuring network	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	6.842866, 45.933188, 10.42462, 47.272886	https://cmr.earthdata.nasa.gov/search/concepts/C3226082890-ENVIDAT.umm_json	The SLF avalanche warning service operates an extensive network of manual measuring sites. The sites are distributed throughout the Swiss Alps and predominantly situated in intermediate altitude zones, between 1000 and 2000 m. Some of the measurement series already span very long periods and are therefore highly valued; the data are also used for climatological and hydrological purposes.    The measuring sites are in fixed locations, which are flat and wind-protected. The observers who perform the measurements are trained and paid by the SLF. Data is collected, as far as possible, from the beginning of November until the end of April and after that until half of the measuring site is snow-free. On some measuring sites event-based measurements are also collected during the summer months. If possible, measurements take place between 7 and 7.30 am local time.      The following variables are measured at all measuring sites:      - snow depth and 24-hour new snow      at numerous sites this additional variable is measured:      - water equivalent of 24-hour new snow (height of the water column in millimeters, if the new snow sample is melted, without changing the base area)          __When using the data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/slf-data-service/)__  __To download live data use our [API](https://measurement-api.slf.ch)__.    __To download data older than 7 days use our [File Download](https://measurement-data.slf.ch)__.	proprietary
 mapss_modis_aerosol_814_1	SAFARI 2000 MAPSS MOD04_L2 Aerosol Summary Data for Southern Africa	ORNL_CLOUD STAC Catalog	2000-02-26	2001-12-31	-14.41, -28.25, 32.9, -7.98	https://cmr.earthdata.nasa.gov/search/concepts/C2789737464-ORNL_CLOUD.umm_json	The MODIS (Moderate Resolution Imaging Spectroradiometer) Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD04_L2 aerosol product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 26, 2000, through December 31, 2001. The process of generating the statistics involves identifying these locations on the MODIS MOD04_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following variables: cloud fraction (land); cloud fraction (ocean); particle effective radius (ocean); optical depth (land and ocean); optical depth (land, corrected); optical depth (ocean, effective average); and optical depth ratio (small ocean). 	proprietary
 mapss_modis_watervapor_815_1	SAFARI 2000 MAPSS MOD05_L2 Water Vapor Summary Data for Southern Africa	ORNL_CLOUD STAC Catalog	2000-02-24	2002-03-04	-14.41, -28.25, 32.9, -7.98	https://cmr.earthdata.nasa.gov/search/concepts/C2789738714-ORNL_CLOUD.umm_json	The MODIS (Moderate Resolution Imaging Spectroradiometer) Atmosphere Group develops remote sensing algorithms for deriving sets of atmospheric parameters from MODIS radiance data. These parameters can be integrated into conceptual and predictive global models. MODIS Atmosphere Products Subset Statistics (MAPSS) are generated over important locations around the world, as one of the ways to increase the scope of application of the MODIS atmospheric parameters. This MAPSS data set contains daily time series of the MODIS MOD05_L2 water vapor product over seventeen (17) AERONET sunphotometer measurement sites in southern Africa for the period February 24, 2000, through March 4, 2002. The process of generating the statistics involves identifying these locations on the MODIS MOD05_L2 product, extracting the values of the pixel corresponding to each coordinate point as well as surrounding pixels falling within a 50 x 50 km box centered on the coordinate point. The data product consists of column water-vapor amounts. During the daytime, a near-infrared algorithm is applied over clear land areas of the globe and above clouds over both land and ocean. Over clear ocean areas, water-vapor estimates are provided over the extended glint area. An infrared algorithm for deriving atmospheric profiles is also applied both day and night for Level 2. The data files are stored as ASCII tables in comma-separated-value (.csv) format. There is one file per site per year for each of the following two variables: total column precipitable water vapor (infrared retrieved) and total column precipitable water vapor (near-infrared retrieved).	proprietary
-marine-fish-occurrences-of-tropical-america_1.0	marine fish occurrences of Tropical America	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	-124.8046875, -5.9657537, -48.8671875, 37.7185903	https://cmr.earthdata.nasa.gov/search/concepts/C2789815416-ENVIDAT.umm_json	combined and cleaned occurrences of marine fish species of the Greater Caribbean and Tropical East Pacific.    Data were obtain from the following sources in 2019/2020:  https://gbif.org  https://idigbio.org  https://biogeodb.stri.si.edu/sftep/en/pages  https://biogeodb.stri.si.edu/caribbean/en/pages	proprietary
 marine_mammal_obs_1	Marine Mammal Observations by Greenpeace	AU_AADC STAC Catalog	1998-12-28	2000-01-24	55.42, -68.59, 179.4, -33.1	https://cmr.earthdata.nasa.gov/search/concepts/C1214313611-AU_AADC.umm_json	Dataset of marine mammal observations made in the Southern Ocean from late 1998 to early 2000.  Further information about the data are included in a word document in the download.  The data are held in excel spreadsheets.  The word document mentioned above lists the column headings for the excel spreadsheets.  The fields in this dataset are:  date time species Number of animals Distance Bearing Heading Initial Cue Behaviour Latitude Longitude Effort status Notes Wind speed Wind direction Actual wind speed Actual wind direction Sea State Cloud cover Visibility Boat speed Boat course Speed made good Course made good Temperature Wave Height Weather Depth Swell height More notes	proprietary
 marsii94_407_1	BOREAS/AES MARS-II 15-minute Surface Meteorological Data: 1994	ORNL_CLOUD STAC Catalog	1994-05-24	1994-09-20	-108.43, 51.08, -97.55, 59.56	https://cmr.earthdata.nasa.gov/search/concepts/C2808090209-ORNL_CLOUD.umm_json	Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Meteorological Automatic Reporting System II autostations.	proprietary
 mas_lv2_561_1	BOREAS Level-2 MAS Surface Reflectance and Temperature Images in BSQ Format	ORNL_CLOUD STAC Catalog	1994-07-21	1994-08-08	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2813525557-ORNL_CLOUD.umm_json	MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.	proprietary
 masccpex_1	Microwave Atmospheric Sounder on Cubesat (MASC) CPEX V1	GHRC_DAAC STAC Catalog	2017-05-27	2017-06-21	-94.4072, 16.541, -69.04, 29.0499	https://cmr.earthdata.nasa.gov/search/concepts/C2658475027-GHRC_DAAC.umm_json	The Microwave Atmospheric Sounder on Cubesat (MASC) CPEX dataset contains products obtained from the MASC instrument onboard the DC-8 aircraft. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May-25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May-24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 27, 2017 through June 21, 2017 and are available in HDF-5 format.	proprietary
 maslv1b_560_1	BOREAS Level-1B MAS Imagery: At-Sensor Radiance, Relative X and Y Coordinates	ORNL_CLOUD STAC Catalog	1994-07-21	1994-08-08	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2808093599-ORNL_CLOUD.umm_json	MAS images, along with the other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR (fraction of Photosynthetically Active Radiation) and LAI (Leaf Area Index).	proprietary
-mass_of_merchantable_branches_of_live_trees-47_1.0	Mass of merchantable branches of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815616-ENVIDAT.umm_json	Dry weight (mass) of branches with a diameter of at least 7 cm from living trees and shrubs  starting at 12cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-mass_of_needles_or_leaves_of_live_trees-49_1.0	Mass of needles or leaves of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816104-ENVIDAT.umm_json	Dry weight (mass) of the needles and leaves of the living trees and shrubs  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-massimo_1.0	MASSIMO	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815474-ENVIDAT.umm_json	MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development.    ![alt text](https://www.envidat.ch/dataset/8fd996d1-aa7e-41b1-ae6d-1192582c62cc/resource/a12e2cfd-da45-4faf-8291-446c5763ac3c/download/massimo2__swissforlab.png)	proprietary
 maun_met_flux_760_1	SAFARI 2000 Meteorological and Flux Tower Measurements in Maun, Botswana, 2000	ORNL_CLOUD STAC Catalog	2000-02-01	2000-09-30	23.55, -19.9, 23.55, -19.9	https://cmr.earthdata.nasa.gov/search/concepts/C2789038485-ORNL_CLOUD.umm_json	To investigate potential contributions of savanna ecosystems to the Earth's carbon balance, an eddy covariance system was used to measure the seasonal variation in carbon dioxide, water vapor, and energy flux at the Maun micrometerological tower site in a broadleaf semi-arid savanna in Southern Africa, approximately 20 km east of Maun in northeastern Botswana.	proprietary
 mawfair1_gis_1	Mawson RAN Fair Sheet Data from HI 99 V5/425 6762/1 (sheet A) scale 1:10 000	AU_AADC STAC Catalog	1987-02-03	1987-02-16	62.871, -67.604, 62.948, -67.555	https://cmr.earthdata.nasa.gov/search/concepts/C1214313537-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Mawson, hand digitised from HI 99 V5/425 6762/1 (sheet A) scale 1:10 000. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID mawsonbathy_gis.	proprietary
 mawfair2_gis_1	Mawson RAN Fair Sheet Data from HI 99 V5/425 6762/1 (sheet B) scale 1:25 000	AU_AADC STAC Catalog	1987-02-03	1987-03-01	62.799, -67.551, 62.912, -67.449	https://cmr.earthdata.nasa.gov/search/concepts/C1214313634-AU_AADC.umm_json	Royal Australian Navy soundings of approaches to Mawson Station. This fair sheet, HI 99 V5/425 6762/ scale 1:25 000, was hand digitised to capture soundings  as point data. The data are not suitable for navigation.  Bathymetric contours derived from these and other soundings are available from the metadata record with ID mawsonbathy_gis.	proprietary
@@ -18255,17 +17722,12 @@ mawsonbathy_gis_1	Bathymetry of Approaches to Mawson Station	AU_AADC STAC Catalo
 mbs_wilhelm_msa_hooh_1	15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS)	AU_AADC STAC Catalog	1984-01-01	1998-12-31	86.082, -69.13, 86.084, -69.12	https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.umm_json	This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy.  This work was completed as part of ASAC project 757 (ASAC_757).	proprietary
 mcdonald_dem_may2012_1	A Digital Elevation Model of McDonald Island derived from GeoEye-1 stereo imagery captured 19 May 2012	AU_AADC STAC Catalog	2012-05-19	2012-05-19	72.533, -53.067, 72.74, -53.003	https://cmr.earthdata.nasa.gov/search/concepts/C1214311211-AU_AADC.umm_json	This dataset consists of: 1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012 2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and 3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM. 4 Contours generated from the DEM.   The DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats. The DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum.  The image products are geotiffs as follows.  McDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution.   McDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution.   McDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution.   McDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution.	proprietary
 mcm_seals_Not provided	Marine and Coastal Management (MCM) - Seal Surveys	CEOS_EXTRA STAC Catalog	1974-04-08	2001-06-01	11.68, -34.98, 26.11, -17.47	https://cmr.earthdata.nasa.gov/search/concepts/C2232477678-CEOS_EXTRA.umm_json	Marine and Coastal Management (MCM) is one of four branches of the  Department       of Environmental Affairs and Tourism. It is the regulatory  authority       responsible for managing all marine and coastal activities.  The seal data set       is a collection of seals shot at-sea cruises, and has  been collected from       cruises around the South African Coast, and currently  contains 2440 records of       1 family (Otariidae).	proprietary
-mean-insect-occupancy-1970-2020_1.0	Mean insect occupancy 1970–2020	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082591-ENVIDAT.umm_json	This dataset contains all data, on which the following publication below is based.    **Paper Citation**:    Neff, F., Korner-Nievergelt, F., Rey, E., Albrecht, M., Bollmann, K., Cahenzli, F., Chittaro, Y., Gossner, M. M., Martínez-Núñez, C., Meier, E. S., Monnerat, C., Moretti, M., Roth, T., Herzog, F., Knop, E. 2022. Different roles of concurring climate and regional land-use changes in past 40 years' insect trends. Nature Communications, DOI: [10.1038/s41467-022-35223-3](https://doi.org/10.1038/s41467-022-35223-3)    Please cite this paper together with the citation for the datafile. Please also refer to this publication for details on the methods.    ## Summary    Mean annual occupancy estimates for 390 insect species (215 butterflies [Papilionoidea, incl. Zygaenidae moths], 103 grasshoppers [Orthoptera], 72 dragonflies [Odonata]) for nine bioclimatic zones in Switzerland. Covers the years 1970-2020 (for butterflies) and 1980-2020 (for grasshoppers and dragonflies). Mean occupancy denotes the average number of 1 km x 1 km squares in a zone occupied by the focal species. Occupancy estimates stem from occupancy-detection models run with species records data hosted and curated by [info fauna](http://www.infofauna.ch). Data on the level of single MCMC iterations of model fitting are included (4000 sampling iterations). The nine bioclimatic zones were defined based on biogeographic regions and two elevation classes (square above or below 1000 m. asl)	proprietary
 medical_bibliography_1	A bibliography of polar medicine related articles	AU_AADC STAC Catalog	1947-01-01	2007-06-06	60, -90, 160, -42	https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json	This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007.  The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981.  Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually.  The publications are divided into the following areas:  Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters  The fields in this dataset are: Author Title Journal Year	proprietary
-mega-plots_1.0	Towards comparable species richness estimates across plot-based inventories - data	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-14.0625, 33.1375512, 42.1875, 72.1818036	https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.umm_json	"The data file refers to the data used in Portier et al. ""Plot size matters: towards comparable species richness estimates across plot-based inventories"" (2022) *Ecology and Evolution*.           This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided.          The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/)."	proprietary
 mendocino_mathison_peak_nff_sr_Not provided	Airborne laser swath mapping (ALSM) data of the San Andreas fault	SCIOPS STAC Catalog	2003-02-05	2003-02-11	-123.81387, 39.31092, -123.720085, 39.333496	https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json	"This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory.  Spatial Data Organization Information -  Direct Spatial Reference: Raster    Raster Object Type: Pixel    Row Count: 1285    Column Count: 4398    Vertical Count: 1  Spatial Reference Information -  Horizontal Coordinate System Definition -      Planar -      Map Projection Name: Lambert Conformal Conic    Standard Parallel: 38.333333    Standard Parallel: 39.833333    Longitude of Central Meridian: -122.000000    Latitude of Projection Origin: 37.666667    False Easting: 6561666.666667    False Northing: 1640416.666667    Planar Coordinate Encoding Method: row and column    Coordinate Representation:      Abscissa Resolution: 6.000000     Ordinate Resolution: 6.000000    Distance and Bearing Representation:     Planar Distance Units: survey feet      Geodetic Model:      Horizontal Datum Name: North American Datum of 1983    Ellipsoid Name: Geodetic Reference System 80    Semi-major Axis: 6378137.000000    Denominator of Flattening Ratio: 298.257222"	proprietary
 met-obs-jmr-stations-1976_1	Meteorological Observations Made At JMR Stations 1976-1977	AU_AADC STAC Catalog	1976-01-01	1977-12-31	124.5, -78.5, 93, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214313660-AU_AADC.umm_json	During the Mirny-Dome C traverse in 1976/77, time was spent at a number of cane sites taking JMR measurements, to determine the precise location. During this time, basic meteorological observations of air temperature and pressure were made and recorded.  These documents have been archived in the records store at the Australian Antarctic Division.	proprietary
 met_profile_SA_729_1	SAFARI 2000 Upper Air Meteorological Profiles, South Africa, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-01	2000-09-30	-10, -41, 31, -24	https://cmr.earthdata.nasa.gov/search/concepts/C2789021046-ORNL_CLOUD.umm_json	The University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km.  This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa.  These sonde launches were made to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaign of 2000.Vaisala RS80 sondes were launched from nine sites in South Africa between August 1, 2000 and September 30, 2000. The launch sites were Pietersburg (changed to Polokwane after 2000), Pretoria (Irene), Bethlehem, Springbok, De Aar, Durban, Cape Town, Port Elizabeth, and Gough Island. The parameters measured by the radiosonde instruments include: pressure, air temperature, relative humidity, wind speed, and wind direction.	proprietary
 met_profile_skukuza_728_1	SAFARI 2000 Upper Air Meteorological Profiles, Skukuza, Dry Seasons 1999-2000	ORNL_CLOUD STAC Catalog	1999-08-14	2000-09-23	31.59, -24.97, 31.59, -24.97	https://cmr.earthdata.nasa.gov/search/concepts/C2789020292-ORNL_CLOUD.umm_json	Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999, and between August 24-September 23, 2000.  The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram.	proprietary
-meteo-at-s17-antarctica-2018-2019_1.0	Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	40.0872222, -69.0244444, 40.0872222, -69.0244444	https://cmr.earthdata.nasa.gov/search/concepts/C2789816331-ENVIDAT.umm_json	This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.    A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.     At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.    The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.	proprietary
-meteo-stillberg_1.1	Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.86716, 46.773573, 9.86716, 46.773573	https://cmr.earthdata.nasa.gov/search/concepts/C3226082620-ENVIDAT.umm_json	# Background information  The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).    #  Station description  The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.    # Data description  We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.	proprietary
 meteo_50_1	Meteorology (OTTER)	ORNL_CLOUD STAC Catalog	1989-05-27	1991-01-07	-123.94, 44.38, -121.68, 45.06	https://cmr.earthdata.nasa.gov/search/concepts/C2804768223-ORNL_CLOUD.umm_json	Meteorology data collected on an hourly basis from stations located near the OTTER sites in 1990 and summarized to monthly data--see also: Canopy Chemistry (OTTER)	proprietary
-meteorological-data-used-to-develop-and-validate-the-bias-detecting-ensemble-bde_1.0	Meteorological data used to develop and validate the bias-detecting ensemble (BDE)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816342-ENVIDAT.umm_json	"These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the ""deterministic"" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations."	proprietary
 metnavcpexaw_1	DC-8 Meteorological and Navigation Data CPEX-AW	GHRC_DAAC STAC Catalog	2021-08-17	2021-09-04	-118.163, 11.8616, -45.6412, 34.73	https://cmr.earthdata.nasa.gov/search/concepts/C2287328798-GHRC_DAAC.umm_json	The DC-8 Meteorological and Navigation Data CPEX-AW dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA DC-8 aircraft during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 17, 2021 through September 4, 2021 in ASCII format.	proprietary
 metnavcpexcv_1	DC-8 Meteorological and Navigation Data CPEX-CV	GHRC_DAAC STAC Catalog	2022-09-02	2022-10-03	-118.1571766, 1.8457575, -14.9310906, 39.340882	https://cmr.earthdata.nasa.gov/search/concepts/C2704142547-GHRC_DAAC.umm_json	The DC-8 Meteorological and Navigation Data CPEX-CV dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These data were gathered during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign was based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and will be conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through October 3, 2022, in ASCII format.	proprietary
 mi_vascular_plants_census_1979_1	Annotated Atlas of the Vascular Flora of Macquarie Island - 1979	AU_AADC STAC Catalog	1978-02-01	1979-02-01	158.76068, -54.78485, 158.96393, -54.47483	https://cmr.earthdata.nasa.gov/search/concepts/C1214313681-AU_AADC.umm_json	The atlas shows the known distribution and abundance of each vascular species on Macquarie Island immediately prior to the commencement of control measures against rabbits in 1978. It gives a baseline against which changes in the vegetation can be monitored. The effects of the introduced vertebrates on the vegetation are discussed. Additional data are given on the habitat, gregarious performance and phenology of some species.	proprietary
@@ -18276,8 +17738,6 @@ misrepimpacts_1	Mission Reports IMPACTS	GHRC_DAAC STAC Catalog	2020-01-08	2023-0
 mod13q1-6.0_NA	MOD13Q1 v006 - Cloud Optimized GeoTIFF	INPE STAC Catalog	2000-02-18	2023-02-02	-81.234129, -40, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C3108204163-INPE.umm_json	The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.0 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.	proprietary
 model_36_1	Forest-BGC Model (OTTER)	ORNL_CLOUD STAC Catalog	1990-01-01	1990-12-31	-123.95, 44.38, -121.68, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804749057-ORNL_CLOUD.umm_json	Steve Running's Forest-BGC Model (v.1991)	proprietary
 model_npp_xdeg_1027_1	ISLSCP II IGBP NPP Output from Terrestrial Biogeochemistry Models	ORNL_CLOUD STAC Catalog	1931-01-01	1987-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785306797-ORNL_CLOUD.umm_json	This data set contains modeled annual net primary production (NPP) for the land biosphere from seventeen different global models. Annual NPP is defined as the net difference of annual carbon uptake (grams CO2/m2/yr) from the atmosphere through photosynthesis by the land vegetation and that lost back to the atmosphere through autotrophic and maintenance respiration. NPP is also related to the Net Ecosystem Exchange (NEE) of carbon accumulated by or lost from the surface by its vegetation and soils. NPP is NEE plus heterotrophic (decomposition) respiration of the vegetation and soils. Only NPP values are included in this data set as some models did not estimate NEE. Data for the mean, standard deviation and coefficient of variation of NPP for the 17 models are provided at spatial resolutions of 1.0 degree and 0.5 degrees. There are two compressed (*.zip) data files with this data set.	proprietary
-modeling-snow-failure-with-dem_1.0	Modeling snow failure with DEM	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816442-ENVIDAT.umm_json	This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.	proprietary
-modeling-snow-saltation-the-effect-of-grain-size-and-interparticle-cohesion_1.0	Modeling snow saltation: the effect of grain size and interparticle cohesion	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.1965332, 45.7304689, 8.7121582, 47.1536142	https://cmr.earthdata.nasa.gov/search/concepts/C2789816540-ENVIDAT.umm_json	"This dataset includes the parallel application and the main results supporting the research article ""Modeling snow saltation: the effect of grain size and interparticle cohesion"" published at the Journal of Geophysical Research: Atmospheres.  The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation."	proprietary
 modis_20day_fast_ice_2	MODIS Composite Based Maps of East Antarctic Fast Ice Coverage	AU_AADC STAC Catalog	2000-03-01	2008-12-31	-10, -72, 172, -63.5	https://cmr.earthdata.nasa.gov/search/concepts/C1214313650-AU_AADC.umm_json	"Maps of East Antarctic landfast sea-ice extent, generated from approx. 250,000 1 km visible/thermal infrared cloud-free MODIS composite imagery (augmented with AMSR-E 6.25-km sea-ice concentration composite imagery when required).  Because of imperfections in the MODIS composite images (typically caused by inaccurate cloud masking, persistent cloud in a given region, and/or a highly dynamic fast-ice edge), automation of the fast-ice extent retrieval process was not possible. Each image was thus classified manually. A study of errors/biases of this process revealed that most images were able to be classified with a 2-sigma accuracy of +/- ~3%. More details are provided in Fraser et al., (2010).  *Version 1.2 with extra QC around the Mawson coast and Lutzow-Holm Bay  The directory named ""pngs"" contains browsable maps of fast-ice extent, in the form of Portable Network Graphics (PNG) images. Each of the 159 consecutive images (20-day intervals from Day Of Year (DOY) 61-80, 2000 to DOY 341-366, 2008) contains a map of fast-ice extent along the East Antarctic coast, generated from MODIS and AMSR-E imagery. The colour scale is as follows:  Dark blue: 		Fast ice, as classified from a single 20-day MODIS composite image Red: 			Fast ice, as classified using the previous or next 20-day MODIS composite images Yellow: 		Fast ice, as classified using a single 20-day AMSR-E composite image White: 			Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product. Light blue: 	Southern ocean/pack ice/icebergs   These maps are also provided as unformatted binary fast ice images, in the directory named ""imgs"". These .img files are all flat binary images of dimension 4300 * 425 pixels. The data type is 8-bit byte. Within the .img files, the value for each pixel indicates its cover: 0: Southern Ocean, pack ice or icebergs, corresponding to light blue in the PNG files. 1: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product, corresponding to white in the PNG files. 2: Fast ice, as classified from a single 20-day MODIS composite image, corresponding to dark blue in the PNG files 3: Fast ice, as classified using a single 20-day AMSR-E composite image, corresponding to yellow in the PNG files 4: Fast ice, as classified using the previous or next 20-day MODIS composite images, corresponding to red in the PNG files  To assist in georeferencing these data, files containing information on the latitude and longitude of each pixel are provided in the directory named ""geo"". These files are summarised as follows:  lats.img:		File containing the latitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels. lons.img:		File containing the longitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels.  The .gpd Grid Point Descriptor file used to build the projection is also included. It contains parameters which you can use for matching your projection.  To refer to the time series, climatology, or maps of average persistence, please reference this paper: Fraser, A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, East Antarctic landfast sea ice distribution and variability, 2000-08, Journal of Climate 25, 4, pp. 1137-1156, 2012  In addition, please cite the following reference when describing the process of generating these maps: Fraser, A. D., R. A. Massom, and K. J. Michael, Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery, Elsevier Remote Sensing of Environment, 114 (12), 2888-2896, doi:10.1016/j.rse.2010.07.006, 2010.  To reference the techniques for generating the MODIS composite images, please use the following reference: Fraser, A. D., R. A. Massom, and K. J. Michael, A method for compositing polar MODIS satellite images to remove cloud cover for landfast sea-ice detection, IEEE Transactions on Geoscience and Remote Sensing, 47 (9), pp. 3272-3282, doi:10.1109/TGRS.2009.2019726, 2009.  Please contact Alex Fraser (adfraser@utas.edu.au) for further information."	proprietary
 modis_MOD04_aerosol_813_1	SAFARI 2000 MODIS MOD04_L2 Aerosol Data, GRANT Format, for Southern Africa	ORNL_CLOUD STAC Catalog	2000-08-20	2000-09-26	-20.64, -42.28, 50.52, 10.08	https://cmr.earthdata.nasa.gov/search/concepts/C2789736376-ORNL_CLOUD.umm_json	The subset of the MODIS MOD04_L2 aerosol product provided in this data set represents the swaths that coincide with known times of the South African Weather Bureau/Service (SAWS) Aerocommanders JRA and JRB research aircraft missions to support aerosol research and validation activities for the SAFARI 2000 region. The MODIS aerosol product monitors the ambient aerosol optical thickness over the oceans globally and over a portion of the continents. Further, the aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. Daily Level 2 data are produced at the spatial resolution of a 10 x 10 1 km (at nadir) pixel array. The daily data files cover the period August 21, 2000, through September 26, 2000. For some data collection dates, there are two or more data files.The MOD04_L2 swath data files included in this data set are from the GSFC DAAC (V4.1.0, Collection 004). The MODIS Level 2 data files were converted from Hierarchical Data Format (HDF) to granule tables (GRANT) format. The GRANT format provides the extracted Scientific Data Set (SDS) in ASCII table form where each pixel (x,y) is represented as a row of data with georeferencing information and each SDS is provided as a separate column in the table. The ASCII tables are in comma-delimited format.	proprietary
 modis_MOD05_watervapor_812_1	SAFARI 2000 MODIS MOD05_L2 Water Vapor Data, Binary Format, for Southern Africa	ORNL_CLOUD STAC Catalog	2000-08-20	2000-09-20	-20.64, -42.28, 50.52, 10.1	https://cmr.earthdata.nasa.gov/search/concepts/C2789735221-ORNL_CLOUD.umm_json	The MODIS precipitable water product consists of vertical column water-vapor amounts in centimeters (cm) at 1 km spatial resolution. The SAFARI 2000 product, provided in flat binary data files, is a subset of the official MODIS Level 2 MOD05 product in EOS Hierarchical Data Format (HDF) format. Specifically, the SAFARI product contains data from daytime-only MODIS granules over southern Africa for the period August 21, 2000, through September 20, 2000. A granule is the data collected over the full MODIS swath in a five-minute period. Further, the SAFARI product contains values generated by the MODIS near-infrared algorithm applied over clear land areas only (determined via the QA bit field). All values were derived from MODIS on the morning-pass Terra satellite. The product is very sensitive to boundary-layer water vapor since it is derived from attenuation of reflected solar light from the surface. This data product is essential to understanding the hydrological cycle, aerosol properties, aerosol-cloud interactions, energy budget, and climate.The MOD05 water vapor data files were converted from their original HDF format to flat binary files for this SAFARI 2000 data set. The conversion was performed using code developed in the Interactive Data Language (IDL) Version 5.5. The following Scientific Data Sets (SDS) are provided in this data set: Latitude; Longitude; Sensor_Zenith; and Water_Vapor_Near_Infrared.	proprietary
@@ -18289,7 +17749,6 @@ modis_h2o_heat_flux_762_1	SAFARI 2000 MODIS Water and Heat Fluxes, Maun, Botswan
 modis_l3_albedo_840_1	SAFARI 2000 MODIS L3 Albedo and Land Cover Data, Southern Africa, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-07-11	2000-10-15	5, -30, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2789103516-ORNL_CLOUD.umm_json	The Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths (0.47mm, 0.55mm, 0.67mm, 0.86mm, 1.24mm, 1.64mm, 2.1mm, and broadband 0.3-0.7mm, 0.3-5.0mm, and 0.7-5.0mm). An exception is that no 2.1mm data for black-sky is being archived at this time. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surface albedo data, are generated at 1-minute resolution on an equal-angle grid. The maps are stored in separate HDF files for each wavelength, each 16-day period and each albedo type (white- and black-sky). Data belonging to black sky and white sky albedo have been zipped separately. This format allows the user to have flexibility to download and store only the data absolutely needed.The One-Minute Land Ecosystem Classification Product is a global (static map) data set of the International Geosphere-Biosphere Programme (IGBP) classification scheme stored on an equal-angle rectangular grid at 1-minute resolution. The dataset is generated from the official MODIS land ecosystem classification dataset, MOD12Q1 for year 2000, day 289 data (October 15, 2000). This dataset is used in generating the spatially complete albedo maps, but is also a stand-alone product designed for use by the user community. The Land Ecosystem Classification Map File product file is stored in Hierarchical Data Format (HDF).	proprietary
 modis_landcover_xdeg_968_1	ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001	ORNL_CLOUD STAC Catalog	2000-10-01	2001-10-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784890070-ORNL_CLOUD.umm_json	This data set, ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001, contains  global land cover classifications (dominant type, classification confidence and fractional cover) generated using a full year of MODerate Resolution Imaging Spectroradiometer (MODIS) data covering the period from October 2000 to October 2001. The objective of the MODIS Land Cover Product is to provide a suite of land cover types useful to global system science modelers by exploiting the information content of MODIS data in the spectral, temporal, spatial, and directional domains. These products describe the geographic distribution of the 17 land cover classification scheme proposed by the International Geosphere-Biosphere Programme (IGBP).	proprietary
 modiscpex_1	Moderate Resolution Imaging Spectroradiometer (MODIS) CPEX V1	GHRC_DAAC STAC Catalog	2017-05-09	2017-07-16	-133.329, -15.7699, -7.96509, 62.0543	https://cmr.earthdata.nasa.gov/search/concepts/C2659160181-GHRC_DAAC.umm_json	The Moderate Resolution Imaging Spectroradiometer (MODIS) CPEX dataset includes measurements gathered by MODIS during the Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. Data are available from May 9, 2017 through July 16, 2017 in netCDF-3 format.	proprietary
-mogli-sdm_1.0	Distribution maps of common woody species for Swiss forests	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816628-ENVIDAT.umm_json	**We used Swiss National Forest Inventory ([NFI](https://www.lfi.ch/index-en.php)) data to model the potential distribution of the most common woody species for the forested area of Switzerland and provide potential distribution maps that fulfill specific quality criteria with regard to predicting performance.**      More details on the methods and results are described in the project summary available [here](https://www.envidat.ch/dataset/07a9c22c-9ec2-4f49-87e2-3b2d73ad81f2/resource/9bfd5308-d9be-4d01-ab78-48d33889e04e/download/mogli_summary.pdf).    **The resulting maps can be viewed in a simple web-GIS application available at:**    [https://www.lfi.ch/produkte/mogli/mogli-en.php](https://www.lfi.ch/produkte/mogli/mogli-en.php)    **Data can be used without restrictions, but the data must be explicitly asked from the contact person of the dataset in order to obtain access.**  This is a requirement to fulfill the needs of reporting towards the funding agencies.	proprietary
 mongu_daily_rainfall_785_1	SAFARI 2000 Daily Rainfall Totals for Mongu, Zambia, 1999-2002	ORNL_CLOUD STAC Catalog	1999-07-01	2002-06-30	23.16, -15.25, 23.16, -15.25	https://cmr.earthdata.nasa.gov/search/concepts/C2789658100-ORNL_CLOUD.umm_json	This data set contains daily rainfall totals (mm) from Mongu, in the Western Province of Zambia. The data were collected with a British standard 5 inch diameter rain gauge in a yard 30 m away from the Meteorological Department building near the Mongu Airport (north of downtown and approximately 20 km from the Kataba Local Forest where the permanent 30 m Mongu tower site is located). Rainfall readings were taken by ZMD staff each morning at 06:00 GMT. These data form the official government rainfall record for Mongu.The data files consist of 3 files, one for each year (July to Jun). Each files contains monthly columns with totals for each day of the month as well as a monthly total. The data files are stored as ASCII text files in comma-separated-value (csv) format.	proprietary
 mongu_fpar_trac_784_1	SAFARI 2000 FPAR TRAC Data for Mongu, Zambia, 1999-2002	ORNL_CLOUD STAC Catalog	1999-08-10	2002-02-23	22.03, -16.87, 24.28, -14.93	https://cmr.earthdata.nasa.gov/search/concepts/C2789655178-ORNL_CLOUD.umm_json	Data from the Tracing Architecture and Radiation of Canopies (TRAC) instrument were processed to determine the fraction of intercepted photosynthetically active radiation (FPAR) at the EOS Validation Core Site in Kataba Local Forest, approximately 20 km south of Mongu, Zambia. Measurements began in 1999 and continued into 2002, with measurements collected about every month. TRAC contains three pyranometers sensitive to PAR wavelengths, with two sensors upward looking and one downward looking. The TRAC instrument was carried along three parallel transects, each 750 m long and spaced 250 m apart, about 0.7 m off the ground on clear days near midday. The sensors measured PAR at 32 Hz, resulting in a horizontal sampling interval of about 1.7 cm (Privette et al., 2002). Each transect was divided into 25 m segments, and Fpar values, with date/time stamp, are reported for each segment. The length and spacing of the transects were chosen to sample an area large enough to be representative of a 1 km MODIS pixel.  PAR transmittance values were determined from the upward viewing pyranometers on the TRAC instrument. Due to the large gaps in the canopy, incident PAR was estimated from the TRAC data as 95% of the maximum PAR transmittance value for each transect. The FPAR values of all the observations were averaged to give segment-average FPAR, and segment average FPAR values were averaged to give transect-average FPAR.The data file is stored as an ASCII text file, in comma-separated-value (csv) format, with column headers.	proprietary
 mongu_irradiance_782_1	SAFARI 2000 Surface Irradiance Measurements, Mongu Tower Site, Zambia, 2000-2002	ORNL_CLOUD STAC Catalog	2000-09-04	2002-12-31	22.03, -16.87, 24.28, -14.93	https://cmr.earthdata.nasa.gov/search/concepts/C2789647857-ORNL_CLOUD.umm_json	This data set contains the top-of-canopy irradiance in the shortwave (0.3-2.8 micron) and photosynthetically active radiation (PAR; 0.4-0.7 micron) wavebands collected with an Eppley Precision Spectral Pyranometer (PSP) and a Skye SKE510 pyranometer, respectively. The instruments were deployed at the top of the 30-m tower in the Kataba Local Forest approximately 20 km south of Mongu in Western Province, Zambia. The data include the hourly mean and maximum values from 0500-1600 GMT (7 a.m. - 6 p.m. local time) and cover the period from September 4, 2000 to December 31, 2002. The data were obtained primarily for EOS validation and energy budget modeling.The Skye SKE510 uses a blue enhanced planar diffused silicon detector and has a fairly even response from 400 to 700 nm. The Eppley PSP is a World Meteorological Organization First Class Radiometer designed for the measurement of sun and sky radiation, totally or in defined broad wavelength bands. It comprises a circular multi-junction wire-wound thermopile. A data logger sampled the sensors at 60-second intervals and recorded the maximum and mean values every 60 minutes throughout the day.The data are contained within a single ASCII text file, in comma-separated-value format, with associated date, time, and QA information.	proprietary
@@ -18298,38 +17757,20 @@ mongu_skukuza_albedo_786_1	SAFARI 2000 Surface Albedo and Radiation Fluxes at Mo
 mongu_skukuza_soil_prop_789_1	SAFARI 2000 Soil Properties, Moisture, and Temp., Skukuza and Mongu, 1999-2001	ORNL_CLOUD STAC Catalog	1999-08-19	2001-12-31	23.25, -25.02, 31.47, -15.44	https://cmr.earthdata.nasa.gov/search/concepts/C2789728240-ORNL_CLOUD.umm_json	Soil moisture and temperature profile sensors were deployed at flux tower sites in Mongu, Zambia and Skukuza, South Africa. In addition, thermal infrared sensors were deployed to monitor surface temperature at the sites, and soil samples were collected for physical property analysis. A heat-flux plate was also installed at 10 cm depth at the Mongu site. The data cover the period variously from August, 1999 to December, 2001.At the Mongu site, three profiles of soil moisture and temperature were obtained to a maximum depth of 125 cm. These profiles were located approximately 30 meters north of the Mongu flux tower, within the Kataba Local Forest. Surface radiometric temperature was measured by thermal infrared sensors deployed on top of the 30-meter tower and on a tree.  At the Skukuza site, two profiles of soil moisture and temperature were obtained to a maximum depth of 40 cm in a Combretum stand. The radiometric temperature of the tree crown and the background surface were monitored by infrared thermocouple sensors deployed on a pole at 2.5 m and 5 m heights. Soil samples were collected at different depths in the vicinity of the soil profiles at each site and were analyzed at CSIR in Pretoria to determine bulk density, texture, and particle size distribution. The data files are stored as ASCII text files, in comma-separated-value (.csv) format. Associated with each data file is a metadata (.txt ) file. Among other information, the metadata files indicate periods of missing data.	proprietary
 mongu_tree_rings_788_1	SAFARI 2000 Tree Ring Data, Mongu, Zambia, Dry Season 2000	ORNL_CLOUD STAC Catalog	1953-01-01	2000-08-31	23.26, -15.45, 23.32, -15.19	https://cmr.earthdata.nasa.gov/search/concepts/C2789727146-ORNL_CLOUD.umm_json	This data set contains tree ring data from three sites located about 25 km of the meteorological station at Mongu, Zambia. Data from about 50 individual trees are reported. In addition, chronologies (or site mean curves) that better represent common influences (e.g., in this study, the climatic signal) were developed for each site based on the individual data (Trouet, 2004; Trouet et al., 2001). The series covers a maximum of 46 years, although most series do not extend longer than 30 years. The data were collected during the SAFARI 2000 Dry Season Field Campaign of August 2000.Ten to 23 samples were taken at each site. Brachystegia bakeriana was sampled at site 1, and Brachystegia spiciformis at sites 2 and 3. The vegetation at all sites underwent primitive harvesting for subsistence earlier the same year, thus samples could be taken from freshly cut trees and no living trees were cut. At all sites, samples consisted of full stem discs. Where possible, samples were taken at breast height (1.3 m) or slightly lower. Growth ring widths were measured to the nearest 0.01 mm using LINTAB equipment and TSAP software (Rinn and Jakel, 1997). Four radii per sample disc were measured. Cross-dating and response function analyses were performed by routine dendrochronological techniques. There are two files for each site, one containing integer values representing tree ring widths (raw data), and the other containing standardized values (chronologies), for each year. The data are stored as ASCII table files in comma-separated-value (.csv) format, with column headers. 	proprietary
 mongu_veg_structure_795_1	SAFARI 2000 Vegetation Structure of Kataba Forest, Zambia, Wet Season 2000	ORNL_CLOUD STAC Catalog	2000-02-15	2000-03-15	23.25, -15.44, 23.25, -15.44	https://cmr.earthdata.nasa.gov/search/concepts/C2789733347-ORNL_CLOUD.umm_json	Tree basal area, percent tree canopy cover, and proportional contribution of main species to canopy cover were measured at 60 sampling points at 50 m intervals along six transects in the vicinity of the MODIS validation site tower in Kataba Forest, near Mongu, Zambia, in late February to early March 2000 as part of the SAFARI 2000 Wet Season Campaign. The aim of the study was to provide a broad description of the tree canopy layer around the tower.Tree and shrub species composition was recorded for each grid and measurements of canopy cover (% and rank) and frequency of occurrence (%) were made. Basal area was estimated at each grid site in a single 360 degree sweep using a basal area prism. Four estimates of canopy cover, oriented north, south, east, and west around the sample point, were taken at each grid site using a spherical densiometer and the data were averaged to give a single value for each grid. Only the canopies of trees and shrubs above 1.5 m height were measured.The data are stored in an ASCII file, in csv format. The file lists all tree and shrub species recorded and provides the proportional contribution of these species to canopy cover in each grid. Total tree basal area (m2 ha-1) and overall tree canopy cover (%) in each grid is also provided. The companion file provides additional vegetation data, graphics, long-term meteorological data, a discussion of the study results, and photographs of the study site.	proprietary
-monitoring-of-ash-trees_1.0	Monitoring of ash trees as part of the Intercantonal Forest Observation Programme	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816750-ENVIDAT.umm_json	In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).  We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)	proprietary
-monitoring-of-lymantria-dispar-lymantria-monacha-and-zeiraphera-griseana_1.0	Monitoring of Lymantria dispar, Lymantria monacha and Zeiraphera griseana and host food quality in the Swiss Alps	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.5778198, 46.3287236, 10.1815796, 46.7443779	https://cmr.earthdata.nasa.gov/search/concepts/C2789815357-ENVIDAT.umm_json	The population dynamics of eruptive moths were monitored with pheromone traps and the composition of the larvae's foodplants were analyzed for water content, nitrogen and total phenolics. Moth catches cover a period of 20 years, leaf analyses 10 years. For Zeiraphera griseana (= Z. diniana) only needle analyses are available. The corresponding data on the moth population dynamics are property of A. Fischlin, ETH Zürich, and will be made available on EnviDat as well.	proprietary
-mortality-16_1.0	Mortality	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815431-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that died or disappeared between two inventories and that were not harvested. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-mortality-of-regeneration-acer-spp-and-fagus-sylvatica_1.0	Mortality of regeneration: Acer spp. and Fagus sylvatica	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	23.5817413, 48.1663717, 23.8097076, 48.2368508	https://cmr.earthdata.nasa.gov/search/concepts/C2789815541-ENVIDAT.umm_json	"### One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.     ### Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.     ## Variables:  * species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica  * species: as above  * dummy: 0 - living individual, 1 - dead individual   * LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]  * tree_age: in years  * avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring	  * dry_mass_g: aboveground and belowground biomass  * DLI: direct light index (measured only under living individuals)  * BLI: diffuse light index (measured only under living individuals)	  * GLI: global light index"	proprietary
-mortality_star-164_1.0	Mortality*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815788-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that died or disappeared between two inventories, but were not cut.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 mosaic-cbers4-brazil-3m-1_NA	CBERS-4/WFI Image Mosaic of Brazil - 3 Months	INPE STAC Catalog	2020-04-01	2020-06-30	-76.6054059, -33.7511817, -27.7877802, 6.3052432	https://cmr.earthdata.nasa.gov/search/concepts/C3108204634-INPE.umm_json	CBERS-4/WFI image mosaic of Brazil with 64m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 15, 16 and 13 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in April 2020 and ending in June 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 CBERS-4 scenes and was generated based on an existing CBERS-4/WFI image collection.	proprietary
 mosaic-cbers4a-paraiba-3m-1_NA	CBERS-4A/WFI Image Mosaic of Brazil Paraíba State - 3 Months	INPE STAC Catalog	2020-07-01	2020-09-30	-38.8134896, -8.3976443, -34.7223714, -5.87659	https://cmr.earthdata.nasa.gov/search/concepts/C3108204719-INPE.umm_json	CBERS-4A/WFI image mosaic of Brazil Paraíba State with 55m of spatial resolution. The mosaic was prepared in order to demonstrate the technological capabilities of the Brazil Data Cube project tools. The false color composition is based on the WFI bands 16, 15 and 14 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2020 and ending in September 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 50 CBERS-4A scenes and was generated based on an existing CBERS-4A/WFI image collection.	proprietary
 mosaic-landsat-amazon-3m-1_NA	Landsat Image Mosaic of Brazilian Amazon Biome - 3 Months	INPE STAC Catalog	2016-07-01	2016-09-30	-75.9138367, -17.1657216, -42.9368587, 5.9260044	https://cmr.earthdata.nasa.gov/search/concepts/C3108204455-INPE.umm_json	Landsat-8/OLI image mosaic of Brazilian Amazon biome with 30m of spatial resolution. The mosaic was prepared in support of TerraClass project. The true color composition is based on the OLI bands 4, 3 and 2 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in July 2016 and ending in September of 2016, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 1200 Landsat/OLI scenes and was generated based on an existing data cube of Landsat images.	proprietary
 mosaic-landsat-brazil-6m-1_NA	Landsat Image Mosaic of Brazil - 6 Months	INPE STAC Catalog	2017-07-01	2018-06-30	-81.6776456, -34.0063116, -27.0765273, 9.9597105	https://cmr.earthdata.nasa.gov/search/concepts/C3108204189-INPE.umm_json	Landsat-8/OLI image mosaic of Brazil with 30m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the OLI bands 6, 5 and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in July 2017 and ending in June 2018, with a best pixel approach called MEDSTK, which uses the middle of the temporal composition interval to select pixels from the closest dates. More information on MEDSTK can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 8000 Landsat/OLI scenes and was generated based on an existing Landsat Image collection.	proprietary
-mosaic-s2-amazon-3m-1_NA	Sentinel-2 image Mosaic of Brazilian Amazon Biome - 3 Months	INPE STAC Catalog	2022-06-01	2022-08-31	-74.8710688, -17.1555658, -43.0123868, 5.763264	https://cmr.earthdata.nasa.gov/search/concepts/C3108204762-INPE.umm_json	Sentinel-2 image mosaic of Brazilian Amazon biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in june 2022 and ending in August 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.	proprietary
-mosaic-s2-cerrado-2m-1_NA	Sentinel-2 Image Mosaic of Brazilian Cerrado Biome - 2 Months	INPE STAC Catalog	2023-11-01	2024-04-30	-61.4214321, -24.7889766, -40.9255552, -1.7795418	https://cmr.earthdata.nasa.gov/search/concepts/C3108204178-INPE.umm_json	Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 02-months of images, starting in November 2023 and ending in April 2024, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.	proprietary
-mosaic-s2-cerrado-4m-1_NA	Sentinel-2 image Mosaic of Brazilian Cerrado Biome - 4 Months	INPE STAC Catalog	2022-06-01	2022-09-30	-60.4781051, -24.7889766, -40.9255552, -1.7795418	https://cmr.earthdata.nasa.gov/search/concepts/C3108204669-INPE.umm_json	Sentinel-2 image mosaic of Brazilian Cerrado biome with 10m of spatial resolution. The mosaic was prepared in support of TerraClass project. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 04-months of images, starting in june 2022 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 14000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.	proprietary
-mosaic-s2-paraiba-3m-1_NA	Sentinel-2 image Mosaic of Brazilian Paraiba State - 3 Months	INPE STAC Catalog	2019-11-01	2020-01-31	-38.8138995, -8.3980636, -34.7220574, -5.876571	https://cmr.earthdata.nasa.gov/search/concepts/C3108204427-INPE.umm_json	Sentinel-2 image mosaic of Brazilian Paraíba State with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the Federal University of Paraíba's Public Policy Studies Program for Early Childhood Education (NEPPS) by supporting the development of the COVID 19/PB Platform: Relations between Health, Territory and Social Protection in times of sanitary crisis, which created the Platform Covid-19/Paraíba: Social and Health Indicator Observatory for SUS and SUAS management. The false color composition is based on the MSI bands 8, 4 and 3 assigned to RGB channels. The temporal composition encompasses 03-months of images, starting in November 2019 and ending in January 2020, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 800 Sentinel-2 scenes and was generated based on an existing Sentinel-2 image collection.	proprietary
-mosaic-s2-yanomami_territory-6m-1_NA	Sentinel-2 image Mosaic of Brazilian Yanomami Indigenous Territory - 6 Months	INPE STAC Catalog	2019-04-01	2022-04-01	-66.5059774, -0.4488209, -61.2800511, 4.3426917	https://cmr.earthdata.nasa.gov/search/concepts/C3108204733-INPE.umm_json	Sentinel-2 image mosaic of Brazilian Yanomami Indigenous Territory with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the ICIT-FIOCRUZ Health Information Laboratory (LIS) a multi-institutional body coordinated by Fiocruz and the ministry of health, by creating a health situation database of the Yanomami Indigenous Land. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in April 2019 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.	proprietary
-mosaic-snow-on-sea-ice-data_1.0	MOSAIC Snow on Sea Ice Data	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	96.3982849, 85, 96.3982849, 85	https://cmr.earthdata.nasa.gov/search/concepts/C3226083009-ENVIDAT.umm_json	Data accompanying David Wagners' Dissertation.   Covers model results and various input from ALPINE3D and SNOWPACK adjusted for sea ice during MOSAiC.	proprietary
-mountain-permafrost-hydrology_1.0	Mountain Permafrost Hydrology	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816262-ENVIDAT.umm_json	This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.	proprietary
-mountland-jura_1	Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	6.433333, 46.866667, 6.433333, 46.866667	https://cmr.earthdata.nasa.gov/search/concepts/C2789816307-ENVIDAT.umm_json	Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.    In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded – sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.    Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46°54’, E 6°23’) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46°52’, E 6°26’), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d’Aubonne, N 46°51’, E 6°37’), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46°47’, E 6°41’). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.    Soil cores were assembled into rectangular PVC boxes of 60  80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope Sàrl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.    A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the modelʼs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.   The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).    #References    1. Puissant, J., Cécillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296–305.  2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473–474(0): 465-472.  3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia (174) : 1425-1435.  4. Peringer A., Siehoff S., Chételat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/  5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/	proprietary
-mr_1.0	RoRCC	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4535933, 47.3601075, 8.4571981, 47.3616191	https://cmr.earthdata.nasa.gov/search/concepts/C2789816324-ENVIDAT.umm_json	"The dataset ""RoRCC"" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:  1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle."	proprietary
 mrmsimpacts_1	Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS V1	GHRC_DAAC STAC Catalog	2022-01-01	2023-03-02	-129.9949951, 20.0050011, -60.0050049, 54.9949989	https://cmr.earthdata.nasa.gov/search/concepts/C2287332555-GHRC_DAAC.umm_json	The Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS dataset contains reflectivity products using the MRMS system during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data are available from January 1, 2022, through March 2, 2023, in netCDF-4 format.	proprietary
 msutls_6	AMSU/MSU Lowstratosphere Day/Month Temperature Anomalies and Annual Cycle	GHRC_DAAC STAC Catalog	1978-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1996545162-GHRC_DAAC.umm_json	The AMSU/MSU Lowstratosphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower stratosphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.	proprietary
 msutlt_6	AMSU/MSU Lowtroposphere Day/Month Temperature Anomalies and Annual Cycle V6	GHRC_DAAC STAC Catalog	1978-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1996545409-GHRC_DAAC.umm_json	The AMSU/MSU Lowtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.	proprietary
 msutmt_6	AMSU/MSU Midtroposphere Day/Month Temperature Anomalies and Annual Cycle V6	GHRC_DAAC STAC Catalog	1978-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1996545587-GHRC_DAAC.umm_json	The AMSU/MSU Midtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the mid-troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.	proprietary
 msuttp_6	AMSU/MSU Tropopause Day/Month Temperature Anomalies and Annual Cycle V6	GHRC_DAAC STAC Catalog	1978-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1996545873-GHRC_DAAC.umm_json	The AMSU/MSU Tropopause Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the tropopause. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.	proprietary
 mt_menzies_sat_1	Mt Menzies Satellite Image Map 1:100 000	AU_AADC STAC Catalog	1997-08-01	1997-08-31	60.3515, -73.5029, 63.4339, -72.9	https://cmr.earthdata.nasa.gov/search/concepts/C1214313653-AU_AADC.umm_json	Satellite image map of Mt Menzies, Mac. Robertson Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1997.  The map is at a scale of 1:100000, and was produced from Landsat TM and SPOT XS scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, and gives some historical text information. The map has both geographical and UTM co-ordinates.	proprietary
-multifaceted-diversity-alps_1.0	Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	4.5922852, 42.8786471, 17.512207, 48.4024632	https://cmr.earthdata.nasa.gov/search/concepts/C3226082602-ENVIDAT.umm_json	This repository contains extensive data for the European Alps:     - Observations of ~3,500 plant species    - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)    - Flora migration rates (categorical) and ecological preferences (continuous indicator values)    - Regional maps of barriers to migration and water bodies at 100-m resolution    - Sampling effort, distance to roads and cities predictors at 100-m resolution    - Present and future abundances over the study region at 1-km resolution (~2,000 species)    - Present and future multifaceted and uniqueness of the European Alps' Flora at  1-km resolution    - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)    - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)    - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them	proprietary
-multiple-realizations-of-daily-swe-swi-and-rain-projections_1.0	Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.3090973, 46.544946, 8.4725189, 46.6591138	https://cmr.earthdata.nasa.gov/search/concepts/C2789816349-ENVIDAT.umm_json	The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.	proprietary
 musondeimpacts_1	Millersville University Upper Air Radiosondes IMPACTS V1	GHRC_DAAC STAC Catalog	2022-01-16	2023-02-28	-76.455, 39.927, -72.503, 43.097	https://cmr.earthdata.nasa.gov/search/concepts/C2516027930-GHRC_DAAC.umm_json	The Millersville University Upper Air Radiosondes IMPACTS dataset contains atmospheric temperature, dew point temperature, wind speed, and wind direction measurements  using Vaisala’s Radiosonde RS41-SGP and Sparv Embedded S1H3 Windsond during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data are available from January 16, 2022 through February 25, 2022 in ASCII format.	proprietary
 mwlezflx_493_1	BOREAS AFM-01 NOAA/ATDD Long-EZ Aircraft Flux Data over the SSA	ORNL_CLOUD STAC Catalog	1994-05-25	1994-09-15	-106.32, 53.42, -104.24, 54.32	https://cmr.earthdata.nasa.gov/search/concepts/C2808091806-ORNL_CLOUD.umm_json	Data include aircraft altitude, wind direction, wind speed, air temperature, potential temperature, water mixing ratio, U and V components of wind velocity, static pressure, surface radiative temperature, downwelling and upwelling total radiation, downwelling and upwelling longwave radiation, net radiation, downwelling and upwelling PAR, greenness index, CO2 concentration, O3 concentration, and CH4 concentration.	proprietary
 myd13q1-6.0_NA	MYD13Q1 v006 - Cloud Optimized GeoTIFF	INPE STAC Catalog	2002-07-04	2023-02-25	-81.234129, -40, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C3108204453-INPE.umm_json	The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.0 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.	proprietary
-n-availability-face-hofstetten_1.0	Nitrogen availability under trees exposed to CO2 enrichment (FACE)	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.502, 47.468, 7.5035, 47.469	https://cmr.earthdata.nasa.gov/search/concepts/C2789815388-ENVIDAT.umm_json	Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.	proprietary
 n_s_dem_248_1	BOREAS DEM Data over the NSA-MSA and SSA-MSA in AEAC Projection	ORNL_CLOUD STAC Catalog	1970-01-01	1989-12-31	-105.23, 53.65, -97.98, 56.14	https://cmr.earthdata.nasa.gov/search/concepts/C2807621661-ORNL_CLOUD.umm_json	AEAC projection of the original DEMs produced by the BOREAS HYD-08 team.	proprietary
-nacl_interfacial_phasechanges_1.0	Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.2213783, 47.5325019, 8.2213783, 47.5325019	https://cmr.earthdata.nasa.gov/search/concepts/C2789816432-ENVIDAT.umm_json	Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.	proprietary
 nalma_1	North Alabama Lightning Mapping Array (LMA) V1	GHRC_DAAC STAC Catalog	2018-12-17		-88.6453, 32.7246, -84.6453, 36.7246	https://cmr.earthdata.nasa.gov/search/concepts/C2683433889-GHRC_DAAC.umm_json	The North Alabama Lightning Mapping Array (NALMA) data are used to validate the Lightning Imaging Sensor (LIS) on the International Space Station (ISS), the Geostationary Lightning Mapper (GLM) instrument, and other current and future lightning measurements. These data are also used in convective storm process studies, including but not limited to validation of convection-resolving models that predict lightning. These NALMA data files are available from December 17, 2019 and are ongoing in ASCII format.	proprietary
 nalmaraw_1	North Alabama Lightning Mapping Array (LMA) Raw Data V1	GHRC_DAAC STAC Catalog	2018-12-17		-88.6453, 32.7246, -84.6453, 36.7246	https://cmr.earthdata.nasa.gov/search/concepts/C2023051335-GHRC_DAAC.umm_json	The North Alabama Lightning Mapping Array (LMA) Raw Data are used to validate the Lightning Imaging Sensor (LIS) on the International Space Station (ISS), the Geostationary Lightning Mapper (GLM) instrument, and other current and future lightning measurements. These data are also used in convective storm process studies, including but not limited to validation of convection-resolving models that predict lightning. These NALMA data files are available from December 17, 2019 and are ongoing in ASCII format.	proprietary
 nam2ds_1	NAMMA TWO-DIMENSIONAL STEREO PROBE AND CLOUD PARTICLE IMAGER V1	GHRC_DAAC STAC Catalog	2006-08-19	2006-09-12	-34.1533, 7.03833, -10.5583, 21.9783	https://cmr.earthdata.nasa.gov/search/concepts/C1979884823-GHRC_DAAC.umm_json	The NAMMA Two-Dimensional Stereo Probe and Cloud Particle Imager dataset consists of data from two probes used to measure the size, shape, and concentration of cloud particles; the two-dimensional stereo probe (2D-S), and the cloud particle imager (CPI). Both of these probes measure particle size distributions and derives extinction, particle concentration, ice water content and particle shape. Both probes provide hi-resolution black and white images of cloud particles. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.	proprietary
@@ -18355,27 +17796,16 @@ namsmart_1	NAMMA SMART-COMMIT MOBILE LABORATORIES V1	GHRC_DAAC STAC Catalog	2006
 namtoga_1	NAMMA TOGA RADAR DATA V1	GHRC_DAAC STAC Catalog	2006-08-15	2006-09-16	-24.8, 13.6, -24.8, 16.4	https://cmr.earthdata.nasa.gov/search/concepts/C1979889418-GHRC_DAAC.umm_json	The NAMMA TOGA Radar Data dataset consists of a collection of products derived from the NASA TOGA radar observations that were collected in the Republic of Cape Verde during the NAMMA campaign. The NASA TOGA radar is a C-band scanning radar with a beam width of 1.65 degrees. The radar was deployed on the southern tip of Sao Tiago (14.92N, 23.48W), the southern-most island in the Cape Verde islands. The radar operated nearly continuously from 15 August through 16 September, 2006, collecting measurements of horizontal radar reflectivity (ZH), radial velocity (VR) and spectral width (SW). These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.	proprietary
 namukatd_1	NAMMA ATD LIGHTNING DATA V1	GHRC_DAAC STAC Catalog	2006-08-14	2006-09-20	-75, -5, 30, 35	https://cmr.earthdata.nasa.gov/search/concepts/C1979889519-GHRC_DAAC.umm_json	The NAMMA ATD Lightning data provided by the UK Meterological Office from multiple outstations contains lightning stroke data, latitude and longitude, accuracy and weighting for fading-in flashes of lightning for the African Coast during the NAMMA experiment. Time is determined by the Arrival Time Difference (ATD) of the reporting stations. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.	proprietary
 namzeus_1	NAMMA LIGHTNING ZEUS DATA V1	GHRC_DAAC STAC Catalog	2006-08-01	2006-10-01	-180, -89.8757, 180, 89.9292	https://cmr.earthdata.nasa.gov/search/concepts/C1979889582-GHRC_DAAC.umm_json	The NAMMA Lightning ZEUS data is provided by World-ZEUS Long Range Lightning Monitoring Network Data obtained from radio atmospheric signals located at thirteen ground stations spread across the European and African continents and Brazil from August 1, 2006 to October 1, 2006. Lightning activity occurring over a large part of the globe is continuously monitored at varying spatial accuracy (e.g. 10-20 km within and >50 km outside the network periphery) and high temporal (1 msec) resolution. Time is determined by the Arrival Time Difference between the time series from the pairs of the receivers. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.	proprietary
-nanoplastics-in-forests_1.0	Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	-23.3789062, 39.6440485, 50.8007813, 68.9131125	https://cmr.earthdata.nasa.gov/search/concepts/C2789816530-ENVIDAT.umm_json	The fate of plastic in the environment is of global concern, because its production recently has  increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on  its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known  about the impact of micro and nanoplastics in forest ecosystems.    The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species  through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest  trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and  examined if they were incorporated in different above- and belowground tissues.     Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C<1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.     We acknowledge that the new method developed might be not sensitive enough to unequivocally detect  mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.	proprietary
-napf-ert-monitoring-data_1.0	Napf ERT monitoring data	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.81963, 47.02481, 7.81963, 47.02481	https://cmr.earthdata.nasa.gov/search/concepts/C2789816636-ENVIDAT.umm_json	The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process.    The data is organized in two zip-files:    *   Napf_Raw_BIN.zip: Raw monitoring data in bin-format  *   Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring line    The zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format    The file names contain following abbreviations:    *   Site: Napf   *   Profile: Hor (horizontal profile), Ver (vertical profile)  *   Configuration: WS (Wenner-Schlumberger configuration)  *   Date: Format YYYY-MM-DD  *   Time: Format hhmm	proprietary
-napf-soil-wetness-monitoring-data_1.0	Napf soil wetness monitoring data	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.8153777, 47.0216958, 7.822845, 47.0262011	https://cmr.earthdata.nasa.gov/search/concepts/C3226082639-ENVIDAT.umm_json	The dataset contains the soil wetness monitoring data from the publication Wicki et al. (2022). It was collected in Wasen i.E. (Napf area, Switzerland).  The monitoring data is quality-controlled and aggregated to hourly values and it is provided for the study period 2019-04-05 to 2022-04-30.    The following information is contained (by column):    * Timestamp (UTC+1 time zone)  * Site: Slope (47.02486 N, 7.81960 E), Flat (47.02302 N, 7.81760 E)  * Sensor type  * Measure: VWC = volumetric water content [m3 m-3], MP = matric potential [hPa], TEMP = temperature [°C], PREC = precipitation [mm]  * Sensor number (per site each sensor is provided a unique identifier)  * Installation depth [m]  * Homogenization flag: If the data is considered homogeneous, it is given the flag 1, else the flag 0 is given  * Sensor value  * Normalized value: Normalization was conducted for VWC (saturation) and MP values    Wicki, A., Lehmann, P., Hauck, C., and Stähli, M.: Impact of topography on in-situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-211, in review, 2022.	proprietary
-nascent-campaign-data-for-motos-et-al-2023_1.0	NASCENT campaign data for Motos et al. 2023	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082684-ENVIDAT.umm_json	"The data are described in detail in the paper ""Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds"" (https://doi.org/10.5194/egusphere-2023-530, 2023).  This dataset includes particle number size distribution data from two DMPSs, chemical composition data from a Tof-ACSM, updraft velocity from an ultrasonic anemometer and a wind lidar, cloud droplet number concentration from a HOLIMO and meteorological data (wind speed and direction, temperature).  Note that aerosol composition from a filter pack system, organiccarbon massfrom a high volume sampler and eBC concentration from a MAAP are available on EBAS and therefore not included here"	proprietary
-naturalness-of-protective-forests_1.0	Naturalness of tree species composition in protective forests	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082740-ENVIDAT.umm_json	Data and scripts used by Scherrer et al. 2023 in the publication 'Maintaining the protective function of mountain forests under climate change by the concept of naturalness in tree species composition'. The analysis is based on data about the tree species composition of the canopy layer in the NFI4 and information about the potential natural forest of the sites based on the NaiS classification system.	proprietary
 nauru99_0	Measurements near Nauru, Micronesia in 1999	OB_DAAC STAC Catalog	1999-06-11		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360517-OB_DAAC.umm_json	Measurements made around the island of Nauru in Micronesia in 1999.	proprietary
 navdc8cpex_1	DC-8 Navigation Data CPEX	GHRC_DAAC STAC Catalog	2017-05-25	2017-06-28	-118.146, 16.8091, -69.2995, 38.1965	https://cmr.earthdata.nasa.gov/search/concepts/C2609922003-GHRC_DAAC.umm_json	The DC-8 Navigation Data CPEX dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA DC-8 aircraft during the Convective Processes Experiment (CPEX) field campaign. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 25, 2017 through June 28, 2017 in ASCII format.	proprietary
 navghepoch_1	Global Hawk Navigation EPOCH	GHRC_DAAC STAC Catalog	2017-07-27	2017-08-31	-124.437, 16.8153, -83.8438, 37.1057	https://cmr.earthdata.nasa.gov/search/concepts/C2199836135-GHRC_DAAC.umm_json	The Global Hawk Navigation EPOCH dataset consists of the real-time navigation and housekeeping data that was acquired by various instruments aboard the Global Hawk during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The data files are available from July 27, 2017 through August 31, 2017 in CSV format with associated KML browse files. 	proprietary
 nbi_veg_maps_787_1	SAFARI 2000 NBI Vegetation Map of the Savannas of Southern Africa	ORNL_CLOUD STAC Catalog	1968-01-01	2000-12-31	8, -35, 43, 0	https://cmr.earthdata.nasa.gov/search/concepts/C2789726555-ORNL_CLOUD.umm_json	The National Botanical Institute (NBI) has mapped woody plant species distribution to provide estimates of individual species contribution to peak leaf area index for designated vegetation types in southern Africa (Rutherford et al., 2000). The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources include published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998); Giess (1971); Wild and Barbosa (1968); Barbosa (1970); and White (1983). Each source map delineates a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final map is made up of six independent sub-regional maps. A cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units is provided.The data set contains six GIS shapefile archives, each containing a shape file for a given region in southern Africa on a 5 x 5 degree grid. An accompanying ASCII file contains the species list associated with the map files. The regional NBI Vegetation Map (a compilation of the 6 independent sub-regional coverages) is provided as a JPEG image.	proprietary
 ncep_met_1deg_1226_1	ISLSCP II Reanalysis Near-Surface Meteorology Data	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785343626-ORNL_CLOUD.umm_json	"This data set for the ISLSCP Initiative II data collection provides near surface meteorological variables, fluxes of heat, moisture and momentum at the surface, and land surface state variables, all with a spatial resolution of 1 degree in both latitude and longitude. There are four temporal categories of data: time invariant and monthly mean annual cycle fields (together referred to as ""fixed"" fields), monthly mean fields, monthly 3-hourly diurnal, and 3-hourly fields. Two types of variables exist in this data; instantaneous fields (primarily state variables), and average fields (primarily flux fields expressed as a rate). The Center for Ocean-Land Atmosphere Studies (COLA) near-surface data set for ISLSCP II was derived from the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmospheric Model Inter-comparison Project (AMIP-II) reanalysis (http://www.cpc.ncep.noaa.gov/products/wesley/reanalysis2/), covering the years from 1979-2003. The data set for ISLSCP II covers the period from 1986 to 1995. The purpose of the reanalysis was to provide an improved version of the original NCEP/National Center for Atmospheric Research (NCAR) reanalysis for General Circulation Model (GCM) validation. To co-register the NCEP/DOE reanalysis on the ISLSCP 1-degree grid, the reanalysis data set was regridded from its native T62 Gaussian grid) resolution (192 x 94 grid boxes globally) to 1-degree ISLSCP II required resolution.There are 136 compressed (.tar.gz) data files with this data set. When extrapolated, the individual data files are in ASCII (.asc) format."	proprietary
 ncsusndimpacts_1	NCSU Soundings IMPACTS	GHRC_DAAC STAC Catalog	2020-02-20	2023-02-12	-78.643, 35.757, -78.623, 35.777	https://cmr.earthdata.nasa.gov/search/concepts/C1995865990-GHRC_DAAC.umm_json	The NCSU Soundings IMPACTS dataset consists of atmospheric-sounding data collected by the North Carolina State University student sounding club. These data include vertical profiles of atmospheric temperature, relative humidity, pressure, wind speed, and wind direction. These rawinsondes were launched from Raleigh, NC in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The sounding data files are available in netCDF-4 format for February 20, 2020, from February 12, 2023. 	proprietary
-nead_0.1 (public request for comments)	Non-Binary Environmental Archive Data (NEAD) format	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.4546355, 47.3605899, 8.4546355, 47.3605899	https://cmr.earthdata.nasa.gov/search/concepts/C2789815434-ENVIDAT.umm_json	"__Acknowledgement__: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.    The NEAD specifications were presented at the __""WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century"" (Virtual Conference)__.     -----------------------    __Summary:__  The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.     __License:__ The NEAD specifications are released to the public domain under a Creative Commons CC0 ""No Rights Reserved"" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions."	proprietary
-neophyte-risk-map-ticino_1.0	Presence probability risk maps neophyte Ticino	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.4320068, 45.822898, 9.1255188, 46.5605915	https://cmr.earthdata.nasa.gov/search/concepts/C3226082886-ENVIDAT.umm_json	943 disturbances in the forest of southern Switzerland have been visited and characterized with various general and specific parameters and the presence absence of woody neophyte species has also been recorded.  A Generalized linear regression modelling approach with a binomial family (link function “logit”) was then used to analyse the effects of these parameters on the presence/absence of the six most widespread neophyte species separately (i.e Ailanthus altissima, Buddleja davidii, R. pseudoacacia, Paulownia tomentosa, Prunus laurocerasus, Trachycarpus fortunei). If needed, the models were refitted with the spmodel R-package to account for the spatial dependence. The best model for every species have been used to predict the risk of invasion on a 25 X 25m grid of 1’773’603 million of points covering the entire forest area under 1’500 m a.s.l. Predictions over this new set of points have been computed with the predict function (v4.2.1; R core Team, 2023) and using the best select model for every neophyte species. The resulting prediction are available as a raster tiff.  These presence probability risk maps for the forest area of the entire canton Ticino provide a practical  tool to be used in combination with the waldmonitoring.ch data allowing to efficiently monitor the spread of woody neophyte species in new disturbances in the forest.	proprietary
-net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1.0	Net primary productivity (NPP) anomalies simulated by 3-PG model for Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815930-ENVIDAT.umm_json	Simulated net primary productivity (NPP) anomalies (percent deviation) in 1961-2018 years relative to the 1961–1990 reference period for _Picea abies_ and _Fagus sylvatica_. NPP was simulated for the species' potential distribution range in Switzerland on a 1 × 1 km grid using 3-PG model.  We first assimilated nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017, into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from.   We then estimated the NPP anomalies by first simulating the growth of _P. abies_ and _F. sylvatica_ monocultures with the average climate observed during the 1961–1990 period, until the age of 40 years (spin-up). The stands were simulated starting as 2-year-old plantations with an initial density of 10,000 trees/ha. Thinning was performed at age 20 and 35 to reach a final density of ca. 1,000 trees/ha at age 40. We then simulated 30 years forced by monthly resolved climatic data from either the 1961–1990 (reference, according to MeteoSwiss) or the 1991–2018 period. We neglected the first 40 years of simulations due to high variation in productivity caused by early stage stand development. To study the impact of climate extremes on NPP, we focused on the deviation in NPP (expressed in percentage difference from the reference period) during the 30 year period (age 41–70).	proprietary
 net_carbon_flux_662_1	Net Carbon Dioxide and Water Fluxes of Global Terrestrial Ecosystems, 1969-1998	ORNL_CLOUD STAC Catalog	1969-01-01	1998-01-01	-162, -45.5, 176, 68.5	https://cmr.earthdata.nasa.gov/search/concepts/C2779678769-ORNL_CLOUD.umm_json	The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting.	proprietary
-net_increment-80_1.0	Net increment	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815660-ENVIDAT.umm_json	Increment including ingrowth minus the mortality.   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-net_increment_star-187_1.0	Net increment*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815851-ENVIDAT.umm_json	Increment with ingrowth minus the mortality.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 newcomb_bay_bathy_dem_1	A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands	AU_AADC STAC Catalog	1997-02-01	2000-02-05	110.512, -66.282, 110.566, -66.256	https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json	A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands  and terrestrial and bathymetric contours derived from the DEM.  The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES.	proprietary
 nexeastimpacts_1	NEXRAD Mosaic East IMPACTS V1	GHRC_DAAC STAC Catalog	2019-12-31	2020-02-29	-85, 32.5, -67.525, 46.475	https://cmr.earthdata.nasa.gov/search/concepts/C1995866059-GHRC_DAAC.umm_json	The NEXRAD Mosaic East IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered  during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic East dataset is composed of Level II data from 19 NEXRAD sites in the eastern U.S.. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 nexmidwstimpacts_1	NEXRAD Mosaic Midwest IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-02-29	-93, 36, -79.025, 45.975	https://cmr.earthdata.nasa.gov/search/concepts/C1995866123-GHRC_DAAC.umm_json	The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered  during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
-niche-partitioning-between-wild-bees-and-honeybees_1.0	Niche partitioning between wild bees and honeybees	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4299469, 47.3172277, 8.6949921, 47.4130345	https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.umm_json	"Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity.    This dataset contains the raw and processed data supporting the findings from the paper: ""Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city"".     The data contains:    1. Bee trait measurements at the species and individual-level  of five functional traits.    2. The values of the feeding niche partitioning (functional dissimilarity to honeybees)     3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites."	proprietary
 nigeria_marine_Not provided	Nigerian Institute for Oceanography and Marine Research - Marine Species	CEOS_EXTRA STAC Catalog	1970-01-01		-5, 4.27417, 5.89333, 6.39722	https://cmr.earthdata.nasa.gov/search/concepts/C2232477671-CEOS_EXTRA.umm_json	The Nigerian Institute for Oceanography and Marine Research (NIOMR), was created from the Marine Research Division of the Federal Department of Fisheries.  The Aquaculture department is mandated to research into the development of Aquaculture, including improvement of transportation devices for juveniles to reduce mortality.  This collection was compiled from publications, and it currently consists of 556 records of 106 families.	proprietary
 nitrogen_deposition_730_1	Nitrogen Deposition onto the United States and Western Europe	ORNL_CLOUD STAC Catalog	1987-01-01	1994-12-31	-124, 25, 44.5, 70.5	https://cmr.earthdata.nasa.gov/search/concepts/C2776873201-ORNL_CLOUD.umm_json	This data set contains data for wet and dry nitrogen-species deposition for the United States and Western Europe. Deposition data were acquired directly from monitoring programs in the United States and Europe for time periods from 1978-1994 for wet deposition and from 1989-1994 for dry deposition and evaluated using similar quality assurance criteria to ensure comparability. A standard geostatistical method (kriging) was used to interpolate data onto a 0.5 x 0.5 degree resolution map for wet and dry deposition.	proprietary
 nlcd_1992_2001_retrofit_Not provided	NLCD 1992/2001 Retrofit Land Cover Change Product	USGS_LTA STAC Catalog	1992-01-01	2001-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567922-USGS_LTA.umm_json	"Developments in mapping methodology, new sources of input data, and changes in the mapping legend for the 2001 National Land Cover Database (NLCD2001) will confound any direct comparison between NLCD2001 and National Land Cover Dataset 1992 (NLCD1992). Users are cautioned that direct comparison of these two independently created land cover products is not recommended. This NLCD 1992/2001 Retrofit Land Cover Change Product was developed to offer users more accurate direct change analysis between the two products.  The NLCD 1992/2001 Retrofit Land Cover Change Product uses a specially developed methodology to provide land cover change information at the Anderson Level I classification scale (Anderson et al., 1976*), relying on decision tree classification of Landsat satellite imagery from circa 1992 and 2001. Unchanged pixels between the two dates are coded with the NLCD01 Anderson Level I class code, while changed pixels are labeled with a ""from-to"" land cover change value. Additional details about this product are available in the metadata included in the multi-zone downloadable zip file. This product is designed for regional application only and is not recommended for local scales."	proprietary
@@ -18384,25 +17814,14 @@ nlcd_2001_ver_2_Not provided	NLCD 2001 Version 2	USGS_LTA STAC Catalog	1970-01-0
 nlcd_2006_Not provided	National Land Cover Database 2006 (NLCD2006)	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567921-USGS_LTA.umm_json	National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States.	proprietary
 noaa_albedo_5year-av_xdeg_959_1	ISLSCP II NOAA 5-year Average Monthly Snow-free Albedo from AVHRR	ORNL_CLOUD STAC Catalog	1985-04-01	1991-03-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784885509-ORNL_CLOUD.umm_json	The objective of this work was to produce a monthly climatology of broadband surface albedos for use in global numerical weather prediction models at the National Centers for Environmental Prediction (NCEP). Monthly means of clear-sky, surface, broadband, snow-free albedos for overhead sun illumination angle were determined using data from a five-year period from April 1985-December 1987 and January 1989-March 1991. The data set is compatible in temporal coverage and spatial resolution with a monthly climatology of green vegetation fraction (Gutman and Ignatov, 1998) delivered earlier and currently in use at NCEP. Three zip files are provided at three spatial resolutions of quarter, half and on degree, each containing 12 data files in standard ESRI ArcGIS ArcInfo Grid format, and 12 data files in ASCII format denoting defifferences between the original data set and the ISLSCP II Land Sea Mask. 	proprietary
 noaasndimpacts_1	NOAA Soundings IMPACTS	GHRC_DAAC STAC Catalog	2020-01-01	2023-03-01	-98.4233, 27.6953, -68.0036, 48.5747	https://cmr.earthdata.nasa.gov/search/concepts/C1995866540-GHRC_DAAC.umm_json	The NOAA Soundings IMPACTS dataset was collected from January 1, 2020, through March 1, 2023, during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The goal of IMPACTS was to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. The data are available in netCDF-4 format. 	proprietary
-non-native-native-plant-interactions-in-australian-grasslands_1.0	Native and no-native plant interactions in Australian grasslands	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	149.3701172, -37.2418448, 150.4083252, -36.2382181	https://cmr.earthdata.nasa.gov/search/concepts/C2789816323-ENVIDAT.umm_json	This dataset contains all data, on which the following publication below is based.    __Paper Citation:__    _Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._    __Please cite this paper together with the citation for the datafile.__      Study area  The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).   The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).   Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).    Experimental design and sampling  The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.   Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.    References  NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).  NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).  Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.  Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.	proprietary
 npolimpacts_1	NASA S-Band Dual Polarimetric Doppler Radar (NPOL) IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-10	2020-02-25	-76.8875, 37.0488, -73.7959, 39.4762	https://cmr.earthdata.nasa.gov/search/concepts/C1995867554-GHRC_DAAC.umm_json	The NASA S-Band Dual Polarimetric (NPOL) Doppler Radar IMPACTS dataset consists of rain rate, reflectivity, Doppler velocity, and other radar measurements obtained from the NPOL radar during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. The goal of IMPACTS was to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The IMPACTS NPOL data are available from January 10, 2020 thru February 25, 2020. Zipped data files are in netCDF-3/CF format and contain corrected radar reflectivity, differential reflectivity, specific differential phase, differential phase, co-polar correlation, and Doppler velocity images.	proprietary
 ns0012bq_482_1	BOREAS NS001 TMS Level-2 Images: Reflectance and Temperature in BSQ Format	ORNL_CLOUD STAC Catalog	1994-04-19	1994-09-16	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929136513-ORNL_CLOUD.umm_json	This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Collection of the NS001 images occurred over the study areas during the 1994 field campaigns.  The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994.	proprietary
 ns001bil_440_1	BOREAS NS001 TMS Level-0 Images in BIL Format	ORNL_CLOUD STAC Catalog	1994-05-24	1994-09-19	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929070415-ORNL_CLOUD.umm_json	The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas.  This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI.  Data collections occurred over the study areas during the 1994 field campaigns.	proprietary
 nsafcovr_252_1	BOREAS Forest Cover Layers of the NSA in Raster Format	ORNL_CLOUD STAC Catalog	1988-01-01	1992-12-31	-98.82, 55.72, -97.83, 56.07	https://cmr.earthdata.nasa.gov/search/concepts/C2807622831-ORNL_CLOUD.umm_json	Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files.	proprietary
 nsf0232042_Not provided	Aeromagnetic and gravity data of the Central Transantarctic Mountains	CEOS_EXTRA STAC Catalog	2003-12-27	2004-01-25	139.27539, -83.95234, 170.21844, -82.35733	https://cmr.earthdata.nasa.gov/search/concepts/C2231555173-CEOS_EXTRA.umm_json	Near complete coverage of the East Antarctic shield by ice hampers geological       study of crustal architecture important for understanding global tectonic and       climate history. Limited exposures in the central Transantarctic Mountains        (CTAM), however, show that Archean and Proterozoic rocks of the shield as well       as Neoproterozoic-lower Paleozoic sedimentary successions were involved in       oblique convergence associated with Gondwana amalgamation. Subsequently, the       area was overprinted by Jurassic magmatism and Cenozoic uplift.               To extend the known geology of the region to ice-covered areas, we conducted an       aeromagnetic survey flown in draped mode by helicopters over the Transantarctic       Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew       >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of       600 m, with average line spacing 2.5 km over most areas and 1.25 km over       basement rocks exposed in the Miller and Geologists ranges. Additional lines       flown to true north, south and west extended preliminary coverage and tied with       existing surveys. Gravity data was collected on the ground along a central       transect of the helicopter survey area.  From December 2003 to January 2004,       the CTAM group flew a helicopter and twin-otter aeromagnetic survey and       collected gravity station data on the ground in profile form.  These data will       be integrated with other geologic and geophysical data in order to extend the       known geology of the region to ice-covered areas.	proprietary
-number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0	Number of natural hazard fatalities per year in Switzerland since 1946	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.6469727, 45.767523, 10.579834, 47.864774	https://cmr.earthdata.nasa.gov/search/concepts/C2789816460-ENVIDAT.umm_json	This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.  The following information is contained (by column and column title):      * year  * total number of hazard fatalities  * number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.  * number of fatalities by debris flow (German: Murgang).  * number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).  * number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).  * number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.  * number of fatalities by lightning (German: Blitz).  * number of fatalities by ice avalanche (German: Eislawine).  * number of fatalities by earthquake (German: Erdbeben).  * number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).    The data was collected based on newspaper research. For more information please refer to     _Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._    The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.    __Restrictions: The data set is not complete.__ Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).    Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).	proprietary
-number_of_forest_edges-124_1.0	Number of forest edges	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816337-ENVIDAT.umm_json	Number of forest edges according to the NFI definition.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-number_of_forest_plots-125_1.0	Number of forest plots	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816350-ENVIDAT.umm_json	Number of forest sample plots (Plots).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-number_of_woody_species_from_40_cm_height-144_1.0	Number of woody species (from 40 cm height)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816548-ENVIDAT.umm_json	Number of species of living trees and shrubs  starting at 40 cm plant height that occur within a 200 m2 sample plot.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-number_of_woody_species_gt_12_cm_dbh-41_1.0	Number of woody species (>= 12 cm DBH)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816627-ENVIDAT.umm_json	Number of tree and shrub species  starting at 12 cm dbh (diameter at breast height) within the 200 m2 sample plot.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-number_of_young_forest_plants_by_damage-209_1.0	Number of young forest plants by damage	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816738-ENVIDAT.umm_json	Number of regeneration trees  starting at 10 cm height up to 11.9 cm dbh with a particular type of damage or with no damage. The attribute is recorded by targeting the next regeneration tree in the centre of the subplot during NFI’s regeneration survey. A regeneration tree may have more than one type of damage, which means it may contribute to the total number of regeneration trees for several different types of damage.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-nutrient-addition-stillberg_1.0	Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.867544, 46.7716544, 9.867544, 46.7716544	https://cmr.earthdata.nasa.gov/search/concepts/C3226082769-ENVIDAT.umm_json	# Background information    The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation.   Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed.    # Experimental design    Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019).    # Data description    The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here:  <https://figshare.com/articles/dataset/Twelve_years_of_low_nutrient_input_stimulates_growth_of_trees_and_dwarf_shrubs_in_the_treeline_ecotone/7025858>	proprietary
 nwrc_amphibianslowermiss_Not provided	A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley	CEOS_EXTRA STAC Catalog	1999-09-05	1999-12-05	-91.95, 31.15, -91.25, 32.4333	https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json	Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV).  By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development.  Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented.  In addition, these forests are subjected to extreme hydrological alterations.  Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV.  Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species.  Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV.  Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide.  Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality.  Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques.  To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges.  In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features.  Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations.  Information was obtained from Janene Lichtenberg for this metadata.	proprietary
 nymesoimpacts_1	New York State Mesonet IMPACTS	GHRC_DAAC STAC Catalog	2020-01-03	2023-03-02	-79.6375, 40.594, -72.1909, 44.9057	https://cmr.earthdata.nasa.gov/search/concepts/C1995873777-GHRC_DAAC.umm_json	The New York State Mesonet IMPACTS dataset is browse-only. It consists of temperature, wind, wind direction, mean sea level pressure, precipitation, and snow depth measurements, as well as profiler Doppler LiDAR and Microwave Radiometer (MWR) measurements from the New York State Mesonet network during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands;  and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The Mesonet network consists of ground weather stations, LiDAR profilers, and microwave radiometer (MWR) profilers. These browse files are available from January 3, 2020, through March 2, 2023, in PNG format.	proprietary
 obrienbay_bathy_dem_1	A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands	AU_AADC STAC Catalog	1997-03-31	1997-03-31	110.516, -66.297, 110.54, -66.293	https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json	A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands.	proprietary
-observational-data-switzerland-2016-2021_1.0	Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021 )	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815389-ENVIDAT.umm_json	This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_      - danger signs    - human triggered avalanches    - rutschblock test results  (still to be added)     - extended column test results    (still to be added)	proprietary
-observed-and-simulated-snow-profile-data-from-switzerland_1.0	Observed and simulated snow profile data	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082908-ENVIDAT.umm_json	This data set includes information on all observed and simulated snow profiles that were used to train and validate the  random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.     The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.	proprietary
-observer-driven-pseudoturnover-in-vegetation-surveys_1.0	Observer-driven pseudoturnover in vegetation surveys	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815537-ENVIDAT.umm_json	"This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, Küchler H, Küchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science.   In the framework of the project ""Monitoring the effectiveness of habitat conservation in Switzerland"", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover.  The excel file contains two sheets:   1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species <0.5 m in height), S (shrub layer; woody species 0.5–3 m in height) and T (tree layer; woody species >3 m in height). ""cf."" indicates uncertain identification, ""aggr."" indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r ≙ <0.1%, + ≙ 0.1% to <1%, 1 ≙ 1% to <5%, 2 ≙ 5% to <25%, 3 ≙ 25% to <50%, 4 ≙ 50% to <75%, 5 ≙ 75% to <100%).  2) File used for the linear mixed effects model."	proprietary
 oldcasey_DSM_2014_1	Digital Surface Model of an area at Old Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 5 February 2014	AU_AADC STAC Catalog	2014-02-05	2014-02-05	110.53727, -66.27963, 110.54104, -66.27865	https://cmr.earthdata.nasa.gov/search/concepts/C1214311216-AU_AADC.umm_json	The Digital Surface Model (DSM) was created by Dr Arko Lucieer of TerraLuma (http://www.terraluma.net/) and the University of Tasmania for the Terrestrial and Nearshore Ecosystems research group at the Australian Antarctic Division (TNE/AAD). The resolution of the DSM is 2 cm. Also included are layers derive from the DSM: hillshade, slope and 20 cm interval contours. An orthophoto was also created. See the metadata record 'Orthophoto of an area at Old Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 5 February 2014' with ID 'oldcasey_ortho_2014'.  The products were requested for Australian Antarctic Science Project 4036:  Remediation of petroleum contaminants in the Antarctic and subantarctic. They cover the drainage area from a fuel spill at Old Casey that occurred in  1982. The products were created from digital photos taken on the 5th February, 2014, with a UAV piloted by Dr Zybnek Malenovsky.  The products were georeferenced to ground control points surveyed using differential GPS by Dr Daniel Wilkins of TNE/AAD. Horizontal Datum: ITRF2000.	proprietary
 oldcasey_buildings_gis_1	GIS Data representing the buildings at the old Casey station	AU_AADC STAC Catalog	1991-08-01	1991-08-01	110.5344, -66.2794, 110.5419, -66.2769	https://cmr.earthdata.nasa.gov/search/concepts/C1214311220-AU_AADC.umm_json	Work commenced on the original Casey station in 1964 and it was fully operational by February 1969. Casey was a novel concept in Antarctic stations at the time with living and sleeping quarters, and some work buildings, in a straight line and connected on the windward side by an aerodynamic corrugated iron tunnel. All were elevated on scaffolding pipe to allow the flow-through of the violent winds common in the region. The tunnel station was decommissioned, demolished and all parts returned to Australia by 1993.  The final data in this dataset is a polygon shapefile representing the buildings at the original (now called 'old') Casey station. Included also are: (i) other files used to create the final shapefile; and (ii) a Readme file with explanation about the procedure used.	proprietary
 olsana_1	OLS ANALOG DERIVED LIGHTNING V1	GHRC_DAAC STAC Catalog	1973-06-01	1991-12-16	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1976712047-GHRC_DAAC.umm_json	The OLS Analog Derived Lightning dataset consists of global lightning signatures from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) that have been analyzed from filmstrip imagery. These signatures show up as horizontal streaks on the film images. The location of each of these streaks has been digitized in order to develop a preliminary database of global lightning activity. Monthly HDF data files are available for June and July 1973; Sept. - Dec. 1977; Jan. - Aug. 1978; Jan. - Dec. 1986; Jan. - Oct. 1987; Dec. 1988; Jan. - Dec. 1990; and Jan. - Dec. 1991.	proprietary
@@ -18410,75 +17829,38 @@ olsdig10_1	OLS DIGITAL DERIVED LIGHTNING FROM DMSP F10 V1	GHRC_DAAC STAC Catalog
 olsdig12_1	OLS DIGITAL DERIVED LIGHTNING FROM DMSP F12 V1	GHRC_DAAC STAC Catalog	1995-05-01	1995-11-30	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1979889790-GHRC_DAAC.umm_json	The OLS Digital Derived Lightning from DMSP F12 dataset consists of global lightning signatures from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) flown on DMSP 5D-2/F12 that have been analyzed from the visible channel imagery. These signatures show up as horizontal streaks on the images. The time and location of each of these streaks have been extracted and are stored by month in HDF data files. Data are available from May 1, 1995 through November 30, 1995.	proprietary
 olson_672_1	LBA Regional Carbon in Live Vegetation, 0.5-degree (Olson)	ORNL_CLOUD STAC Catalog	1960-01-01	1980-01-01	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2776933680-ORNL_CLOUD.umm_json	"This data set is a subset of Olson et al. (1985, 2000) ""Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation."" This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 N to 25 S, longitude 30 to 85 W). The data are in ASCII GRID format.""Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation"" is a computerized database used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups. The map is derived from patterns of preagricultural vegetation, modern areal surveys, and intensive biomass data from research sites. Work on the database was begun in 1960 and completed in 1980.Ecosystem complexes are defined for each 0.5-degree grid cell, reflecting the major climatic, topographic, and land-use patterns. Numeric codes are assigned to each vegetation type. Classifications include natural as well as human managed/modified complexes such as mainly cropped, residential, commercial, and park. The complexes are ranked by estimated organic carbon in the mass of live plants given in units of kilograms of carbon per square meter. Counting the cells of each type and adding their areas give total area estimates for the ecosystem complexes. Multiplying by carbon estimates gives corresponding estimates of carbon by ecosystem complex with in the LBA study area. The results help define the role of the terrestrial biosphere in the global carbon cycle.Information about the ecosystem classifications, as well as the procedure used to create the LBA subset can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/olson/comp/olson_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.Carbon in Live Vegetation is a computerized database, used to generate a global vegetation map of 44 different land ecosystem complexes (mosaics of vegetation or landscapes) comprising seven broad groups."	proprietary
 one_deg_biomass_754_1	SAFARI 2000 1-Degree Estimates of Burned Biomass, Area, and Emissions, 2000	ORNL_CLOUD STAC Catalog	2000-08-01	2000-09-30	-18, -36, 56, 0	https://cmr.earthdata.nasa.gov/search/concepts/C2789029387-ORNL_CLOUD.umm_json	A new method is used to generate spatial estimates of monthly averaged biomass burned area and spatial and temporal estimates of trace gas and aerosol emissions from open fires in southern Africa. Global burned area data for the year 2000 (GBA2000) supplemented with the Along Track Scanning Radiometer (ATSR) fire count data are employed to quantify the area burned at 1-km resolution by using a fractional vegetation cover map derived from satellite observations.	proprietary
-open-science-support-at-wsl_1.0	Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.4546488, 47.3605728, 8.4546488, 47.3605728	https://cmr.earthdata.nasa.gov/search/concepts/C2789815825-ENVIDAT.umm_json	"This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.     Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.  EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.    EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.  For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.    Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.    _License_: This poster is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 ""No Rights Reserved"" international license. You can reuse this poster in any way you want, for any purposes and without restrictions."	proprietary
 orbview_3_Not provided	Orbview-3	USGS_LTA STAC Catalog	2003-01-01	2007-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567912-USGS_LTA.umm_json	OrbView-3 satellite images were collected around the world between 2003 and 2007 by Orbital Imaging Corporation (now GeoEye) at up to one-meter resolution.  The OrbView-3 data set includes 180,000 scenes of one meter resolution panchromatic, black and white, and four meter resolution multi-spectral (color and infrared) data, providing high resolution data useful for a wide range of science applications.  The spacecraft ceased operation on April 23, 2007 and decayed on March 13, 2011 via a controlled reentry into the broad area Pacific Ocean.	proprietary
-oriental-beech-spectral-and-trait-data_1.0	Oriental and European beech spectral, traits and genetics data	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.35, 48.65, 7.35, 48.65	https://cmr.earthdata.nasa.gov/search/concepts/C3226082588-ENVIDAT.umm_json	The dataset includes leaf spectroscopy, leaf traits and genetic data for oriental and european beech trees at two mature forest sites (Allenwiller in France and Wäldi in Switzerland) sampled in summer 2021 and 2022 for top and bottom of canopy leaves.	proprietary
 ornl_lai_point_971_1	ISLSCP II Leaf Area Index (LAI) from Field Measurements, 1932-2000	ORNL_CLOUD STAC Catalog	1932-01-01	2000-12-31	-156.67, -54.5, 172.75, 71.3	https://cmr.earthdata.nasa.gov/search/concepts/C2784892799-ORNL_CLOUD.umm_json	Leaf Area Index (LAI) data from the scientific literature, covering the period from 1932-2000, have been compiled at the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) to support model development and validation for products from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument. There is one data file which consists of a spreadsheet table, together with a bibliography of more than 300 original-source references. Although the majority of measurements are from natural or semi-natural ecosystems, some LAI values have been included from crops (limited to a sub-set representing different crops at different stages of development under a range of treatments). Like Net Primary Productivity (NPP), Leaf Area Index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange. Modeling and validation of coarse scale satellite measurements both require field measurements to constrain LAI values for different biomes (typical minimum, maximum values, phenology, etc.). Maximum values for point measurements are unlikely to be approached or exceeded by area-weighted LAI, which is what satellites and true spatial models are estimating.	proprietary
 otdlip_1	OPTICAL TRANSIENT DETECTOR (OTD) LIGHTNING V1	GHRC_DAAC STAC Catalog	1995-04-13	2000-03-23	-180, -70, 180, 70	https://cmr.earthdata.nasa.gov/search/concepts/C1979889849-GHRC_DAAC.umm_json	The Optical Transient Detector (OTD) records optical measurements of global lightning events in the daytime and nighttime. The data includes individual point (lightning) data, satellite metadata, and several derived products. The OTD was launched on 3 April 1995 aboard the Microlab-1 satellite into a near polar orbit with an inclination of 70 degrees with respect to the equator, at an altitude of 740 km.	proprietary
 oxygen-isotopes-plateau-1984_1	7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984	AU_AADC STAC Catalog	1978-01-01	1984-12-31	100, -75, 130, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214313700-AU_AADC.umm_json	A total of nine stations were sampled for oxygen isotopes during the 1984 spring traverse to the Antarctic Plateau. The aim of this program was to take a number of samples from a core or a pit, at stations of known accumulation over a particular period, to see how far inland the annual cycles could accurately be traced. The samples were not taken at ice movement stations, but at canes each 2km along the line, to avoid sampling the accumulation, and thus isotope disturbance resulting from parking the vans beside the IMS poles in 1978 and 1979.  The accumulation for the cane at each sampled station was calculated for the six years since 1978, and the total multiplied by 7/6 to give the sampling depth required to cover 7 years. Seventy samples were taken at each station, i.e. approximately 10 per year. At most stations a PICO drill was used to obtain a core, and the samples cut with a stainless steel knife on the stainless sink in the living van. At the southern end of the line where the accumulation is much lower, the samples were taken from the wall of a pit, as the small length of core for each sample did not provide enough melt. The snow was sampled in the pits by sliding a flat sheet of galvanized iron into the snow at each interval starting at the top, and scraping the snow above this into a melt jar. Isotopic contamination of samples from both these methods should be negligible. All samples were melted in plastic jars, and then transferred into 5Oml plastic bottles. A total of 630 samples from 9 stations were returned to Australia for oxygen isotope analysis, carried out in Melbourne by Ted Vishart, Dick Marriot, and Gao Xiangqun.  The station/cane labels for the sample sites were:  A028 V140/4 (near GC30) V230/4 (near GC37) V270/1 (near GC38) V300/1 (near GC39) V350/1 (near GC40) V400/1 (near GC41) V450/1 (near GC42) V630/1 (near GC47)  The columns in the spreadsheet are:  Sequence Number Core depth (metres) Oxygen isotope value (the number is a ratio of O18 per ml of O16, expressed as a percentage (but as parts per 1000 instead of 100))	proprietary
 p3metnavimpacts_1	P-3 Meteorological and Navigation Data IMPACTS	GHRC_DAAC STAC Catalog	2020-01-12	2023-02-28	-95.243, 33.261, -64.987, 48.237	https://cmr.earthdata.nasa.gov/search/concepts/C1995868137-GHRC_DAAC.umm_json	The P-3 Meteorological and Navigation Data IMPACTS dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data are available in ASCII-ict format from  January 12, 2020, through February 28, 2023.	proprietary
-p_pet_500m_1.0	Average precipitation and PET over Switzerland at 500m resolution	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789815390-ENVIDAT.umm_json	"Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution.  Units are mm per year.  Files are GeoTIFF rasters, and can be read in R using the command raster(""pcp_ch_longterm_yr_avg.tif), after installing packages ""raster"" and ""rgdal""."	proprietary
 panpfcov_283_1	BOREAS Prince Albert National Park Forest Cover Data in Vector Format	ORNL_CLOUD STAC Catalog	1978-01-01	1994-12-31	-106.8, 53.56, -105.99, 54.33	https://cmr.earthdata.nasa.gov/search/concepts/C2846961321-ORNL_CLOUD.umm_json	Detailed canopy, understory, and ground cover, height, density, and condition information for PANP in the western part of the BOREAS SSA in vector form.	proprietary
 parprbimpacts_1	NCAR Particle Probes IMPACTS	GHRC_DAAC STAC Catalog	2020-01-18	2023-02-28	-95.243, 33.261, -64.987, 48.237	https://cmr.earthdata.nasa.gov/search/concepts/C1995868627-GHRC_DAAC.umm_json	The NCAR Particle Probes IMPACTS dataset consists of data collected from six instruments on the NASA P-3 aircraft, the SPEC Hawkeye Cloud Particle Imager (CPI), the Hawkeye Fast Cloud Droplet Probe (FastCDP), the Hawkeye Two-Dimensional Stereo Probe (Hawkeye2D-S), the SPEC Two-Dimensional Stereo probe (2D-S), and two SPEC High Volume Precipitation Spectrometers (HVPS3). The 2D-S and HVPS3 are two-dimensional optical array probes that record images of particles that travel through their sampling area. The recorded images are then analyzed to produce particle size distributions from 20 microns to 3 centimeters in diameter.  The FastCDP is a forward scattering instrument designed to measure the size and concentration of cloud droplets between 2 and 50 microns in diameter.  The CPI is a high-resolution imager with a 256-level color depth.  No particle concentration estimates have been attempted with the CPI. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands;  and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data files are available in netCDF-4 format, as well as browse imagery available in PNG format, from January 18, 2020, through February 26, 2020, and January 14, 2022 through February 28, 2023.	proprietary
 pedestrian_gentoo_1	Effects of human activity on Gentoo penguins on Macquarie Island	AU_AADC STAC Catalog	2002-10-20	2003-03-20	158.77029, -54.78327, 158.97079, -54.47961	https://cmr.earthdata.nasa.gov/search/concepts/C1214311242-AU_AADC.umm_json	This project empirically measures the effects of human activity on the behaviour and reproductive success of Gentoo penguins on Macquarie Island. This was achieved by 1) collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of guard, creche and moult, and 2) collecting reproductive success data (chicks raised to creche age per nesting pair) for gentoo penguins colonies in areas of high and low human activity. Information produced includes minimum approach guidelines.  As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).  This work was carried out as part of ASAC project 1148 (ASAC_1148).  The fields in this dataset are:  Sample Date Breeding Phase Stimulus Type Colony Focal birds tape number Wide angle tape number Location within colony Weather Time Windspeed Temperature Precipitation Cloud Pre-approach control Approach Post-approach control Maximum approach distance	proprietary
 pedestrian_king_1	Effects of human activity on King penguins on Macquarie Island	AU_AADC STAC Catalog	2002-10-20	2003-03-20	158.76892, -54.78168, 158.96667, -54.47802	https://cmr.earthdata.nasa.gov/search/concepts/C1214311218-AU_AADC.umm_json	This project empirically measures the effects of human activity on the behaviour of King penguins on Macquarie Island, under ASAC project 1148. This was achieved by collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation and guard. Information produced includes minimum approach guidelines.  As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).  The fields in this dataset are:  Sample Date Breeding Phase Approach Colony Focal birds tape number Wide angle tape number Weather Time Windspeed Temperature Precipitation Cloud Pre-approach control Post-approach control Maximum approach distance	proprietary
 pedestrian_royal_1	Effects of human activity on Royal penguins on Macquarie Island	AU_AADC STAC Catalog	2002-10-20	2003-03-20	158.76755, -54.78247, 158.95981, -54.47802	https://cmr.earthdata.nasa.gov/search/concepts/C1214311223-AU_AADC.umm_json	This project empirically measures the effects of human activity on the behaviour, heart rate and egg-shell surface temperature of Royal penguins on Macquarie Island, as part of ASAC project 1148. This was achieved by collecting behavioural and physiological responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation, guard, creche and moult. Both single person and group approaches were also investigated. Information produced includes minimum approach guidelines.   As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002).  Some notes about some of the fields in this dataset:  Temp file refers to whether or not egg shell surface temperature was also recorded for the sample, with the code below refering to the file name.  Neighbour refers to the behavioural control file for each sample (neighbouring nests did not recieve an artificial egg, and provide a behavioural control for responses to human approaches without the scientific treatment).  Nest refers to the randomly used nest markers for each sample.  Heart rate refers to whether heart rate was concurrently recorded with behaviour on the sample (both stored on Hi-8 tape).  Stimulus refers to whether single persons or groups of persons (5 -7, recorded within each sample) were used for the human approaches.  Environment refers to whether approaches were conducted from colony sections abuting pebbly beach or from poa tussock environs (tussock approaches less  than 50 m of the poa / pebbly beach junction).  The code system for nest simply refers to the numbered tag placed at the nest (using three colours, g=green, w=white, b=brown) which were used randomly.  The fields in this dataset are:  Sample Date Breeding Phase Stimulus Type Environment Colony Nest Tape Heart Rate Temp File Neighbour	proprietary
-pfynwald_2016	Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	7.61192, 46.30284, 7.61192, 46.30284	https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.umm_json	To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.	proprietary
-pfynwaldgasexchange_1.0	2013-2020 gas exchange at Pfynwald	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	7.6105556, 46.3001905, 7.6163921, 46.3047564	https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json	Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds.   All metadata can be found in the attached documents.	proprietary
 phipsimpacts_1	Particle Habit Imaging and Polar Scattering Probe (PHIPS) IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-18	2023-02-28	-95.243, 33.261, -64.987, 48.237	https://cmr.earthdata.nasa.gov/search/concepts/C1995874351-GHRC_DAAC.umm_json	The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format.	proprietary
-phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0	Phosphorus and nitrogen leaching from beech forest soils	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.927478, 50.3518, 10.26725, 52.838967	https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.umm_json	Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding  fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.  Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019   with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.   For more details please refer to the publication.	proprietary
 photo_mosaic_laurens_or_1	Heard Island, Laurens Peninsula, Coastal Orthophoto Mosaic derived from Non-Metric Photography	AU_AADC STAC Catalog	1980-01-01	2000-12-31	73.23, -53.05, 73.41, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214311224-AU_AADC.umm_json	The orthophoto mosaic is a rectified georeferenced image of the Heard Island, Laurens Peninsula Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric cameras (focal length unknown).	proprietary
 photo_mosaic_laurens_or_TopoMapping_1	Heard Island, Laurens Peninsula, Topographic Mapping from Orthophoto Mosaic derived from Non-Metric Photography	AU_AADC STAC Catalog	1980-01-01	1997-12-31	73.23, -53.05, 73.41, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214311225-AU_AADC.umm_json	The Heard Island, Laurens Peninsula, Topographic Data was mapped from Ortho-rectified non-metric photography.  The data consists of Coastline, Crater, Volcano, Island, Lagoon, Water Storage and Watercourse datasets digitised from the photography.	proprietary
-photogrammetric-drone-data-and-derived-ground-classification-wolfgang-arelen_1.0	Photogrammetric Drone Data and derived Ground Classification Wolfgang Arelen	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.828043, 46.8307111, 9.8549938, 46.8425715	https://cmr.earthdata.nasa.gov/search/concepts/C3226082622-ENVIDAT.umm_json	We conducted two drone flights with the Wingtra and DJI Phantom 4 RTK drones in Davos Wolfgang Arelen, on 25.08.2021. The data was processed with the Agisoft Metashape Professional Software.The Wingtra point cloud was further processed to derive a ground classification in individual LASTools and Terrasolid workflows.	proprietary
-photogrammetric-drone-data-dorfberg_1.0	Photogrammetric Drone Data Dorfberg	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8224404, 46.8123513, 9.847963, 46.8276943	https://cmr.earthdata.nasa.gov/search/concepts/C3226082629-ENVIDAT.umm_json	The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).	proprietary
-photogrammetric-drone-data-gruenboedeli_1.0	Photogrammetric Drone Data Grüenbödeli	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8807722, 46.8397097, 9.8985161, 46.8545346	https://cmr.earthdata.nasa.gov/search/concepts/C3226082635-ENVIDAT.umm_json	We conducted various drone flights at Grüenbödeli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.   The following products are available for download:   - DSM 10cm resolution  - Orthomosaic 3cm/25mm resolution  - Snow Raster 10cm resolution  - original RGB images	proprietary
-photogrammetric-drone-data-latschuelfurgga_1.0	Photogrammetric Drone Data Latschuelfurgga	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.7673884, 46.7935833, 9.793926, 46.8112506	https://cmr.earthdata.nasa.gov/search/concepts/C3226082666-ENVIDAT.umm_json	To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.	proprietary
-photogrammetric-drone-data-schuerlialp_1.0	Photogrammetric Drone Data Schürlialp	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8921656, 46.7231571, 9.928736, 46.7468439	https://cmr.earthdata.nasa.gov/search/concepts/C3226082739-ENVIDAT.umm_json	The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.	proprietary
-photogrammetric-drone-data-wolfgang-arelen_1.0	Photogrammetric Drone Data Wolfgang Arelen	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8252503, 46.8303515, 9.8550571, 46.8397178	https://cmr.earthdata.nasa.gov/search/concepts/C3226082799-ENVIDAT.umm_json	We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.	proprietary
-pine-insects-along-elevational-gradients_1.0	Pine insects along elevational gradients	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.8994141, 45.5935412, 10.1513672, 47.3189659	https://cmr.earthdata.nasa.gov/search/concepts/C2789815415-ENVIDAT.umm_json	The colonization of cut pine stems by wood-inhabiting insects was investigated at various elevations. The study sites were located in the regions of Aosta Valley (Italy), Valais (Switzerland), and Grisons (Switzerland). In each region, there were two gradients in pine (Pinus sylvestris) forests, with three study sites at 900 m, 1200 m, and 1600 m a.s.l. each. Vital trees were felled in late autumn and the stems were colonized by pioneering xylophagous insects and their natural enemies next spring. Pieces of these stems were cut and exposed in emergence traps in a greenhouse. In each region the survey was done in two consecutive years.    Please contact author for terms of use.	proprietary
-place-attachment-dataset_1.0	Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.5267639, 47.3535362, 8.5360336, 47.360746	https://cmr.earthdata.nasa.gov/search/concepts/C3226082958-ENVIDAT.umm_json	This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.	proprietary
-plan-statements-for-external-consistency-analysis_1.0	Plan statements for external consistency analysis: Evidence from Bucharest’s spatial plans	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	25.4311515, 44.1362934, 26.7495109, 44.8260721	https://cmr.earthdata.nasa.gov/search/concepts/C2789816344-ENVIDAT.umm_json	The present dataset is part of the published scientific paper Bacău, S., Grădinaru, S. R., & Hersperger, A. M. (2020). Spatial plans as relational data: Using social network analysis to assess consistency among Bucharest’s planning instruments. Land Use Policy, 92.  The goal of this paper was to first develop a theoretical framework for external consistency assessment in spatial plans and then to test the framework with ten spatial plans of the Bucharest (Romania) region. Specifically, the paper has the following workflow: (i) to develop a framework for consistency assessment covering four categories of external consistency; (ii) to extract relevant plan statements from all plans on the four categories; (iii) to assign one-way, symmetrical and contradictory relationships between the extracted plan statements; and (iv) to assess consistencies, inconsistencies and contradictions between plans using directed and valued network analyses. All results were then validated by applying questionnaires to local experts.   The study focuses on a sample consisting of 10 spatial plans of Bucharest that: (1) are currently in force, (2) have spatial implications, (3) involve different administrative levels and (4) come from different planning sectors. The list of the reviewed planning documents can be found in Table 2 of the paper. The framework of consistency assessment contains 24 items, which can be found in Table 1 of the paper. All planning documents were read in respect to all items of the framework in order to extract plan statements used in the analysis. As a result, we provide the plan statement extracted from 10 plans on the 24 items of the framework. All data is in Romanian. The data was discussed qualitatively in the research paper.	proprietary
-planning-efficacy-computation-based-on-ahp_1.0	Planning efficacy computation based on AHP	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.5803223, 47.3762671, 8.5803223, 47.3762671	https://cmr.earthdata.nasa.gov/search/concepts/C2789815581-ENVIDAT.umm_json	The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.	proprietary
-planning-efficacy-questionnaire-and-interviews_1.0	Planning efficacy - questionnaire and interviews	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	4.284668, 45.4138765, 12.7441406, 56.170023	https://cmr.earthdata.nasa.gov/search/concepts/C2789816053-ENVIDAT.umm_json	The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the interviews, the survey, the report explaining survey and a xlxs table to save results.	proprietary
-planning-intention-in-copenhagen-urban-region_1.0	Planning intention in Copenhagen urban region	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	11.7498779, 55.4115741, 12.7056885, 56.1529123	https://cmr.earthdata.nasa.gov/search/concepts/C2789816167-ENVIDAT.umm_json	The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.	proprietary
-planning-intention-in-hannover-urban-region_1.0	Planning intention in Hannover urban region	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.4688416, 52.1961653, 10.2186584, 52.546635	https://cmr.earthdata.nasa.gov/search/concepts/C2789816241-ENVIDAT.umm_json	The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.	proprietary
-planning-intentions-in-strategic-plans-of-european-urban-regions_1.0	Planning intentions in strategic plans of European urban regions	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-13.6230469, 34.9647481, 28.9160156, 62.7987017	https://cmr.earthdata.nasa.gov/search/concepts/C2789816301-ENVIDAT.umm_json	"The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions.   The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete “incidents” or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:  	Date when the information was filled in.  	Name of the urban region and analysed strategic spatial plan .  	A list of all planning intentions contained in a plan, with each PI being addresses as follows:  	Name of PI as it appears in the plan  	Translated name of the PI (i.e. short name for easy understanding of the meaning)  	Explanation regarding the meaning of the PI  	Why the PI is considered a priority for the urban region  	Spatial information on the PI (text and cartographic representations).  In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading."	proprietary
-planning-intentions-lyon_1.0	Planning intentions Lyon	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	4.7412872, 45.6763368, 5.0310516, 45.8296515	https://cmr.earthdata.nasa.gov/search/concepts/C2789816325-ENVIDAT.umm_json	The present dataset is the summary of each planning intention (name, type, development pattern, land use, and link with governance and supra regionel conditions), the explanation of interest, and the mapping from plan.	proprietary
-plant-orthoptera-trophic-networks-lif3web-projet_1.0	Plant-Orthoptera trophic networks (Lif3web projet)	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816397-ENVIDAT.umm_json	The study of ecological networks along environmental gradients has so far been limited by the difficulty of collecting large-scale dataset of comparable interactions. Here, we compiled 48 plant–orthoptera interaction networks at multiple locations across the Swiss Alps (i.e. along six elevation gradient). Trophic interactions were obtained by applying next-generation sequencing methods (e.g. DNA metabarcoding) on insect feces. Together with interaction data, we also provide data of the functional trait measurement  (i.e. plant leaves traits and insect mandibular strength) expected to influence the realization of the interaction. Species inventories, feces samples and functionals traits were collected during the summer 2016 and 2017. Lab work and network reconstruction were completed in 2019.	proprietary
 plant_soil_c_n_783_1	SAFARI 2000 Plant and Soil C and N Isotopes, Southern Africa, 1995-2000	ORNL_CLOUD STAC Catalog	1995-03-01	2000-03-28	19.17, -27.75, 31.75, -14.42	https://cmr.earthdata.nasa.gov/search/concepts/C2789651811-ORNL_CLOUD.umm_json	This data set contains measurements of the concentration and stable carbon (13C/12C) and nitrogen (15N/14N) isotope ratios of plant (leaves, roots and fungi) and soil samples from southern Africa. The study sites in Zambia, Botswana, Namibia, and South Africa are located along the Kalahari Transect precipitation gradient. Some of the sites were relatively undisturbed while others had different intensities of cultivation, domestic grazing, and fires. The data were collected to detect patterns of N cycling along precipitation and grazing gradients, including N2 fixation by legumes. Data from different multiple projects are included. The plants and soils were sampled mainly in the wet season of years 1995, 1999, and 2000, with most of the data collected during the SAFARI 2000 Kalahari Wet Season Field Campaign in February and March of 2000. Some grass samples were collected in the dry season of year 2000 (from Mongu-dambo and Sua Pan grassland sites). Soil and plant samples were analyzed in a laboratory for %C, %N, d13C, and d15N with an Optima isotope ratio mass spectrometer coupled to an elemental analyzer. The stable isotope ratios are expressed using standard delta notation in units per mil. The isotope ratios are expressed relative to the international standard PDB (Pee Dee Belemnite) for carbon and atmospheric N2 for nitrogen samples. The carbon and nitrogen contents are expressed in percentage weight of the dry sample.The data files contain numerical and character fields of varying length separated by commas (.csv format).	proprietary
 plotchem_420_1	Calculated Leaf Carbon and Nitrogen, 1992 (ACCP)	ORNL_CLOUD STAC Catalog	1992-06-18	1992-09-01	-89.76, 42.49, -68.74, 45.22	https://cmr.earthdata.nasa.gov/search/concepts/C2776829507-ORNL_CLOUD.umm_json	Study plot canopy chemistry values were calculated from leaf chemistry and litterfall weight values. Average leaf concentrations of nitrogen and carbon were used to investigate how reflectance varies with chemistry.  	proprietary
 plotspec_544_1	Site AVIRIS Images, 1992 (ACCP)	ORNL_CLOUD STAC Catalog	1992-06-02	1992-08-20	-121.76, 29.7, -68.74, 45.22	https://cmr.earthdata.nasa.gov/search/concepts/C2776854217-ORNL_CLOUD.umm_json	AVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated  with estimated canopy carbon and nitrogen content.	proprietary
-plutonium-239-240-in-southern-italy_1.0	Plutonium-239+240 and sediment yield data for a small catchment in Southern Italy	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	16.9628906, 39.1672646, 16.9628906, 39.1672646	https://cmr.earthdata.nasa.gov/search/concepts/C3226082671-ENVIDAT.umm_json	Quantifying the rates of soil redistribution worldwide poses a significant challenge, which has been addressed using various methods such as direct sediment measurements, models, and the use of isotopic, geochemical, and radionuclide tracers. Among these tracers, the isotope of Plutonium, specifically 239+240Pu, is a relatively recent addition to the study of soil redistribution. However, there is still a lack of direct validation for 239+240Pu as a tracer for soil redistribution. To address this gap, we conducted a study in Southern Italy using a unique sediment yield dataset that extends back to the initial fallout of 239+240Pu. Soil samples were collected from the catchment area as well as undisturbed reference sites, and 239+240Pu was extracted, measured using ICP-MS, and converted into soil redistribution rates.	proprietary
 pmhailclim_1	Passive Microwave Hail Climatology Data Products V1	GHRC_DAAC STAC Catalog	1998-01-01	2023-03-31	-179, -89, 179, 89	https://cmr.earthdata.nasa.gov/search/concepts/C2196515446-GHRC_DAAC.umm_json	The Passive Microwave Hail Climatology Data Products are gridded estimates of the annual frequency of severe hailstorm occurrence, as retrieved from satellite-borne passive microwave imagery. These data products can be useful for weather and climatological research related to storms, as well as applications involving risk management and emergency management. The dataset files are available in netCDF-3 format, as well as hail climatology maps in PNG format, from January 1, 1998, through March 31, 2023.	proprietary
 pnet_4_and_5_817_1	PnET Models: Carbon, Nitrogen, Water Dynamics in Forest Ecosystems (Vers. 4 and 5)	ORNL_CLOUD STAC Catalog	1992-01-01	2003-04-21	-72.25, 42.37, -72.25, 42.37	https://cmr.earthdata.nasa.gov/search/concepts/C2956545280-ORNL_CLOUD.umm_json	PnET (Photosynthetic / EvapoTranspiration model) is a nested series of models of carbon, water, and nitrogen dynamics in forest ecosystems. The models can be used to predict transient responses in net primary production (NPP), carbon and water balances, net nitrogen (N) mineralization and nitrification and N leaching losses, resulting from changes in climate, N deposition, tropospheric ozone and land use as well as variation in species composition. The models have been developed and validated in the Northeastern U.S. at both the site and grid level (to 1-km resolution) at the Complex Systems Research Center, University of New Hampshire, by John Aber and colleagues.	proprietary
 pnet_m_bgc_818_1	PnET-BGC: Modeling Biogeochemical Processes in a Northern Hardwood Forest Ecosystem	ORNL_CLOUD STAC Catalog	2000-11-05	2001-12-31	-71.75, 43.94, -71.75, 43.94	https://cmr.earthdata.nasa.gov/search/concepts/C2956545421-ORNL_CLOUD.umm_json	This archived model product contains the directions, executables, and procedures for running PnET-BGC to recreate the results of: Gbondo-Tugbawa, S.S., C.T. Driscoll , J.D. Aber and G.E. Likens. 2001. The evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. Water Resources Research 37:1057-1070Gbondo-Tugbawa et al,. 2001 Excerpt from Abstract: An integrated biogeochemical model (PnET-BGC) was formulated to simulate chemical transformations of vegetation, soil, and drainage water in northern forest ecosystems. The model operates on a monthly time step and depicts the major biogeochemical processes, such as forest canopy element transformations, hydrology, soil organic matter dynamics, nitrogen cycling, geochemical weathering, and chemical equilibrium reactions involving solid and solution phases. The model was evaluated against soil and stream data at the Hubbard Brook Experimental Forest, New Hampshire. Model predictions of concentrations and fluxes of major elements generally agreed reasonably well with measured values, as estimated by normalized mean error and normalized mean absolute error. Model output of soil base saturation and stream acid neutralizing capacity were sensitive to parameter values of soil partial pressure of carbon dioxide, soil mass, soil cation exchange capacity, and soil selectivity coefficients of calcium and aluminum. PnET-BGC can be used as a tool to evaluate the response of soil and water chemistry of forest ecosystems to disturbances such as clear-cutting, climatic events, and atmospheric deposition.PnET-BGC, was used to investigate inputs and dynamics of S in a northern hardwood forest at the Hubbard Brook Experimental Forest (HBEF) (Gbondo-Tugbawa et al., 2002). The changes in soil S pools and stream-water were simulated to assess the response 22 SO4 to both atmospheric S deposition and forest clear-cutting disturbances. Watershed studies across the northeastern United States have shown that stream losses of exceed atmospheric sulfur (S) deposition. Understanding the processes responsible for this additional source of S is critical to quantifying ecosystem response to ongoing and potential future controls on SO2 emission.	proprietary
 polar_star_0	Optical measurements taken in the Southern Ocean in 2002	OB_DAAC STAC Catalog	2002-03-17		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360619-OB_DAAC.umm_json	Optical measurements taken in the Southern Ocean in 2002	proprietary
-pollination-experiment-insect-traits_1.0	Pollination experiment: insect traits	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	8.4038544, 47.3003738, 8.6702728, 47.4380272	https://cmr.earthdata.nasa.gov/search/concepts/C2789816549-ENVIDAT.umm_json	Understanding the interplay of local and landscape-scale drivers of plant-pollinator interactions is crucial to maintaining pollination services in urban environments. The data contains plant-pollinator interactions changed across two independent gradients of local flowering plant species richness and landscape-scale urbanisation level (proportional area of impervious surface within a 500-m radius) in 24 home gardens in the city of Zurich, Switzerland. The data also contains the trait values (tongue length, body size and activity time) of all visiting wild- and honeybees.	proprietary
 population_counts_BI_1	Adelie penguin population counts for Bechervaise, Verner and Petersen Islands, Mawson	AU_AADC STAC Catalog	1971-10-01	2005-02-01	62.8, -67.59, 62.82, -67.58	https://cmr.earthdata.nasa.gov/search/concepts/C1214313706-AU_AADC.umm_json	Intermittent Adelie penguin population counts for Bechervaise, Verner and Petersen Islands, Mawson since 1971.  Data include counts of occupied nests for the post 1990/91 data conducted on or about 2nd December. Data collected prior to this were obtained from ANARE Research Notes or field note books. These counts may not have been performed at the 'optimal' time for occupied nests counts, and when this is the case have been adjusted according to a 'correction' factor.  The post 1990/91 data were completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project.  The fields in this dataset are:  Year Bechervaise Island Counts Verner Island Counts Petersen Island Counts Date Season occ nests (occupied nests)	proprietary
-potential-driving-factors-of-urban-transformations-of-austin-over-25-years_1.0	Potential driving factors of urban transformations of Austin over 25 years	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-97.7493287, 30.2794116, -97.7493287, 30.2794116	https://cmr.earthdata.nasa.gov/search/concepts/C2789816648-ENVIDAT.umm_json	In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured.  In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.	proprietary
 potential_veg_xdeg_961_1	ISLSCP II Potential Natural Vegetation Cover	ORNL_CLOUD STAC Catalog	1992-04-01	1993-04-01	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784887174-ORNL_CLOUD.umm_json	This data set was developed to describe the state of the global land cover in terms of 15 major vegetation types, plus water, before alteration by humans. It forms a complement to the historical croplands data set developed by Ramankutty and Foley (1999). By overlaying the two, one can determine the extent to which natural vegetation has been cleared for cultivation. This data set can be used directly within spatially-explicit climate and biogeochemical models. There are four total files in this data set. Two files contain the land cover types representing potential natural vegetation before human alteration, and two other files contain those points in the original data set submitted by the Principal Investigator that have been modified in order to match the land/water mask of the ISLSCP Initiative II.The geographic distribution of contemporary land cover types can be derived from remotely-sensed data. However, humans now dominate much of the world and there is little evidence of the pre-human-settlement natural vegetation or Potential Natural Vegetation (PNV). PNV, as defined here, does not necessarily represent the world's natural pre-human-disturbance vegetation. Rather, our definition of PNV represents the world's vegetation cover that would most likely exist now in equilibrium with present-day climate and natural disturbance, in the absence of human activities.	proprietary
 potential_vegetation_684_1	LBA Regional Potential Vegetation, 5-min (Ramankutty and Foley)	ORNL_CLOUD STAC Catalog	1900-01-01	1992-01-01	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2777327573-ORNL_CLOUD.umm_json	The data set consists of a subset for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N) of the 5-min resolution Global Potential Vegetation data set developed by Navin Ramankutty and Jon Foley at the University of Wisconsin. Data are available in both ASCII GRID and binary image file formats.The original map was derived at a 5-min resolution and contains natural vegetation classified into 15 types.	proprietary
 pre_post_fire_refl_757_1	SAFARI 2000 Pre- and Post-fire Reflectance near Kaoma, Zambia, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-24	2000-09-04	24.8, -14.79, 24.8, -14.79	https://cmr.earthdata.nasa.gov/search/concepts/C2789035484-ORNL_CLOUD.umm_json	The main goal of this study was to analyze the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes of surface features by the development of relationships between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat ETM+ channels 2, 3, and 4).	proprietary
-predicted-cloud-droplet-numbers-davos-wolfgang_1.0	Predicted cloud droplet numbers Davos Wolfgang	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789815435-ENVIDAT.umm_json	Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).	proprietary
-pref-dep-hills_1.0	Preferential deposition of snow and dust over hills: governing processes and relevant scales	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.0864258, 46.3999881, 7.2619629, 46.6720565	https://cmr.earthdata.nasa.gov/search/concepts/C2789815520-ENVIDAT.umm_json	"Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the  governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios.    This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed  in the original paper. For additional information, please refer to ""Preferential deposition of snow and dust over hills: governing processes and relevant scales"" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019."	proprietary
-preprocessing-antarctic-weather-station-aws-data-in-python_1.0	Preprocessing Antarctic Weather Station (AWS) data in python	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, -60	https://cmr.earthdata.nasa.gov/search/concepts/C3226083020-ENVIDAT.umm_json	There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.    The following data sources are taken into account:    * AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)  * ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division  * AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)  * BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)  * CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)  * IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph  * JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)  * NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)  * Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication  * Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)  * PANGAEA: World Data Center (e.g. König-Langlo, 2012)    __Important notes __    * __Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.__  * Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.  * No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.    Acknowledgements  --------------------------  We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.    References  ---------------  Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East  Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020    Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.    König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener  Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.  775173	proprietary
-present-weather-sensor-klosters_1.0	Present Weather Sensor Klosters	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.880413, 46.869019, 9.880413, 46.869019	https://cmr.earthdata.nasa.gov/search/concepts/C2789816030-ENVIDAT.umm_json	A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of  a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).	proprietary
-production-de-biogaz-a-partir-d-engrais-de-ferme-en-suisse_1.0	Production de biogaz à partir d’engrais de ferme en Suisse	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816300-ENVIDAT.umm_json	L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.	proprietary
 prsondecpexaw_1	Puerto Rico Radiosondes CPEX-AW V1	GHRC_DAAC STAC Catalog	2021-08-24	2021-09-28	-67.6051, 17.8794, -67.0027, 18.4477	https://cmr.earthdata.nasa.gov/search/concepts/C2516026892-GHRC_DAAC.umm_json	The Puerto Rico Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 24, 2021 through September 28, 2021 in ASCII format, with associated browse Skew-T graphs in PNG format.	proprietary
-pv_snow_mountain_1.0	Dataset on PV Production in Snow Covered Mountains	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816321-ENVIDAT.umm_json	"### Overview  The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication ""The bright side of PV production in snow-covered mountains"", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.    ### Format  All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.   This is the case for:    - Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),   - Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),   - Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,   - Electricity production from three of our validation sites (/Validation/WSL),   - Measured irradiance for two validation sites (/Validation/ASRB)  The ‘Metadata’ documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.     ### Structure  The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the __‘DataProcessing’__ and the __‘functions’__ folder. Functions are called in the different Mains.    __‘InputsFromMatlab’__ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.    __‘Outputs’__ contains the output files from the different mains (matching names, Main_CHallpixels.m  Prod_CHallpixels)    __‘Publication_figures’__ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.    __‘Validation’__ contains the data sets used in the model validation:    - Electricity production from three of our validation sites (/WSL),   - Measured irradiance for two validation sites (/ASRB)    __Electricity__ production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.     __Paper Citation:__   > _Annelen Kahl; Jérôme Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_"	proprietary
-r-script-first-stage-sampling_1.0	"R script and input data for ""ALL-EMA sampling design"""	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082897-ENVIDAT.umm_json	License: GPL-v2    The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space.    The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward.    Ecker, K.T., Meier, E.S. & Tillé, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.	proprietary
 r04laifd_293_1	BOREAS RSS-04 1994 Southern Study Area Jack Pine LAI & FPAR Data	ORNL_CLOUD STAC Catalog	1993-08-14	1994-08-05	-105.32, 53.65, -104.64, 53.99	https://cmr.earthdata.nasa.gov/search/concepts/C2813387526-ORNL_CLOUD.umm_json	Contains Decagon Ceptometer estimates of LAI and fPAR. Contains LI-COR LAI-2000 estimates of leaf area index and mean tip angle.	proprietary
 r07elaid_294_1	BOREAS RSS-07 LAI, Gap Fraction, and FPAR Data	ORNL_CLOUD STAC Catalog	1993-08-09	1994-09-17	-106.2, 53.63, -98.19, 55.94	https://cmr.earthdata.nasa.gov/search/concepts/C2807643505-ORNL_CLOUD.umm_json	Contains daily green fpar, LAI, needle-to-shoot area ratio, clumping index, PAI, and foliage-to-total area index for tower and auxiliary sites for IFC's 1, 2, and 3. Also contains effective LAI measurements acquired along RSS-07 transects in the BOREAS study area.	proprietary
 r11sunpd_297_1	BOREAS RSS-11 Ground Sunphotometer Data	ORNL_CLOUD STAC Catalog	1994-01-01	1996-12-30	-106.07, 53.2, -97.83, 55.75	https://cmr.earthdata.nasa.gov/search/concepts/C2807644583-ORNL_CLOUD.umm_json	This table contains measurements from the automatic sun photometers operated by RSS-11 (Markham).	proprietary
 r12sunpd_299_1	BOREAS RSS-12 Automated Ground Sunphotometer Measurements in the SSA	ORNL_CLOUD STAC Catalog	1994-05-25	1994-09-19	-105.33, 53.75, -105.27, 53.8	https://cmr.earthdata.nasa.gov/search/concepts/C2807644739-ORNL_CLOUD.umm_json	This table contains measurements from the ground sun photometers operated by RSS12 (Wrigley).	proprietary
 r19cas94_537_1	BOREAS RSS-19 1994 CASI At-Sensor Radiance and Reflectance Images	ORNL_CLOUD STAC Catalog	1994-02-07	1994-09-15	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929159252-ORNL_CLOUD.umm_json	CASI images from the Chieftain Navaho aircraft taken in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance,2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR and spectral albedo.	proprietary
 r19cas96_538_1	BOREAS RSS-19 1996 CASI At-Sensor Radiance and Reflectance Images	ORNL_CLOUD STAC Catalog	1996-07-18	1996-07-30	-106.32, 53.42, -97.24, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929159820-ORNL_CLOUD.umm_json	CASI images from the Chieftain Navaho aircraft collected in order to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. The overall objective of the CASI deployment was to observe the seasonal change in the radiometric reflectance properties of the boreal forest landscape. CASI data include the following: 1) canopy bidirectional reflectance, 2) canopy biochemistry, 3) spatial variability, and 4) estimates of up and downwelling PAR spectral albedo.	proprietary
-r3pg-an-r-package-for-simulating-forest-growth_1.0	r3PG – An r package for simulating forest growth	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816336-ENVIDAT.umm_json	An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.	proprietary
 r7laifpa_442_1	BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites	ORNL_CLOUD STAC Catalog	1994-04-09	1994-09-10	-115.41, 48.83, -93.29, 61.01	https://cmr.earthdata.nasa.gov/search/concepts/C2929107528-ORNL_CLOUD.umm_json	The BOREAS RSS-07 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of LAI from remotely sensed images.  Ground measurements of LAI and FPAR absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-AUG-1993 to 19-SEP-1994.	proprietary
-raclets-backward-trajectories_1.0	Backward  Trajectories	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789816376-ENVIDAT.umm_json	Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.	proprietary
-radar-wind-profiler-davos-wolfgang_1.0	RADAR Wind profiler Davos Wolfgang	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789816529-ENVIDAT.umm_json	The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.	proprietary
-radiosondes_1.0	Radiosondes	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789816623-ENVIDAT.umm_json	Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.	proprietary
-ramerenwald-close-range-remote-sensing-benchmark_1.0	Ramerenwald Close Range Remote Sensing Benchmark	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.4514582, 47.3614125, 8.452338, 47.3619648	https://cmr.earthdata.nasa.gov/search/concepts/C3226082632-ENVIDAT.umm_json	Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.  Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.	proprietary
 ramp Building Footprint Dataset - Accra, Ghana_1	ramp Building Footprint Dataset - Accra, Ghana	MLHUB STAC Catalog	2020-01-01	2023-01-01	-0.2498695, 5.573258, -0.1898803, 5.6471722	https://cmr.earthdata.nasa.gov/search/concepts/C2781412605-MLHUB.umm_json	This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,330 tiles and 40,786 buildings. The original dataset was sourced from the [Open Cities AI Challenge Dataset](https://doi.org/10.34911/rdnt.f94cxb) before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.	proprietary
 ramp Building Footprint Dataset - Barishal, Bangladesh_1	ramp Building Footprint Dataset - Barishal, Bangladesh	MLHUB STAC Catalog	2020-01-01	2023-01-01	90.3039757, 22.6475678, 90.3893401, 22.7800298	https://cmr.earthdata.nasa.gov/search/concepts/C2781412722-MLHUB.umm_json	This chipped training dataset is over Barishal and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,024 tiles and 41,248 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001597B000). Dataset keywords: Urban, Peri-urban, River	proprietary
 ramp Building Footprint Dataset - Bentiu, South Sudan_1	ramp Building Footprint Dataset - Bentiu, South Sudan	MLHUB STAC Catalog	2020-01-01	2023-01-01	29.7779526, 9.2285638, 29.8314928, 9.3421338	https://cmr.earthdata.nasa.gov/search/concepts/C2781412757-MLHUB.umm_json	This chipped training dataset is over Bentiu and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,789 tiles and 22,396 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (104001004DAECE00). Dataset keywords: Refugee Settlement, Rural. 	proprietary
@@ -18504,20 +17886,11 @@ ramp Building Footprint Dataset - Wa, Ghana_1	ramp Building Footprint Dataset -
 rasipanam_1	REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PANAMA, PANAMA V1	GHRC_DAAC STAC Catalog	1998-01-01	2011-12-31	-81.88, 3.13, -77.88, 9.13	https://cmr.earthdata.nasa.gov/search/concepts/C1979892078-GHRC_DAAC.umm_json	The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Panama, Panama dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Panama, Panama. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.	proprietary
 rasipapag_1	REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF PAPAGAYO, COSTA RICA V1	GHRC_DAAC STAC Catalog	1998-01-01	2011-12-31	-93, 7, -85.38, 12.13	https://cmr.earthdata.nasa.gov/search/concepts/C1979892350-GHRC_DAAC.umm_json	The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Papagayo, Costa Rica dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Papagayo, Costa Rica. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.	proprietary
 rasitehuan_1	REGIONAL AIR-SEA INTERACTION (RASI) GAP WIND AND COASTAL UPWELLING EVENTS CLIMATOLOGY GULF OF TEHUANTEPEC, MEXICO V1	GHRC_DAAC STAC Catalog	1998-01-01	2011-12-31	-105, 5, -93.38, 16.38	https://cmr.earthdata.nasa.gov/search/concepts/C1979892406-GHRC_DAAC.umm_json	The Regional Air-Sea Interactions (RASI) Gap Wind and Coastal Upwelling Events Climatology Gulf of Tehuantepec, Mexico dataset was created using an automated intelligent algorithm which identified gap wind and coastal ocean upwelling events using two satellite-based microwave datasets. The Cross-Calibrated Multi-Platform (CCMP) ocean surface wind data product was used for wind data while the Optimally Interpolated Sea Surface Temperatures (OISST) data product provided by Remote Sensing Systems was used for sea surface temperatures. Data is available from January 1, 1998 through December 31, 2011 for Gulf of Tehuantepec, Mexico. The RASI datasets are products resulting from DISCOVER, a NASA MEaSUREs-funded project.	proprietary
-raw-data-publication-crossresistance-in-ash-new-phytologist_1.0	Raw data-Publication cross-resistance in ash - New Phytologist	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082646-ENVIDAT.umm_json	What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.    Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites.    When and where was the data created / extracted: Summer 2020-2021	proprietary
 raxpolimpacts_1	Rapid X-band Polarimetric Radar (RaXPol) IMPACTS	GHRC_DAAC STAC Catalog	2022-01-29	2023-01-25	-74.732, 41.289, -69.761, 43.439	https://cmr.earthdata.nasa.gov/search/concepts/C3181083175-GHRC_DAAC.umm_json	The Rapid X-band Polarimetric Radar (RaXPol) IMPACTS dataset consists of data measured from the RaXPol instrument during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The RaXPol dataset consists of various reflectivity variables. RaXPol data are available from January 29, 2022, through January 25, 2023, in netCDF-4 format.	proprietary
-re-analysed-regional-avalanche-danger-levels-in-switzerland_1.0	Re-analyzed regional avalanche danger levels in Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226082703-ENVIDAT.umm_json	The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.     D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.     For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).    For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:  1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.   2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.   3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).     All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.    In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.  - The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.  -  For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.	proprietary
 readac_d_408_1	BOREAS/AES READAC 15-minute Surface Meteorological Data	ORNL_CLOUD STAC Catalog	1994-05-24	1994-09-20	-102.32, 52.7, -102.32, 52.7	https://cmr.earthdata.nasa.gov/search/concepts/C2808090390-ORNL_CLOUD.umm_json	Contains 15 minute surface meteorology data collected during the 1994 field campaigns by the Atmospheric Environment Service Remote Environmental Automatic Data Acquisition Concept (READAC) autostations.	proprietary
 reg_aeac_284_1	BOREAS Regional DEM in Raster Format and AEAC Projection	ORNL_CLOUD STAC Catalog	1993-08-01	1996-12-31	-111, 50.09, -93.5, 58.98	https://cmr.earthdata.nasa.gov/search/concepts/C2807638336-ORNL_CLOUD.umm_json	Based on the GTOPO30 DEM produced by the USGS EDC.  The BOREAS region was extracted from the GTOPO30 data and reprojected by BOREAS staff into the AEAC projection.	proprietary
 regsoilr_285_1	BOREAS Regional Soils Data in Raster Format and AEAC Projection	ORNL_CLOUD STAC Catalog	1991-01-01	1991-12-31	-111, 50.09, -93.5, 59.98	https://cmr.earthdata.nasa.gov/search/concepts/C2807638473-ORNL_CLOUD.umm_json	This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS).  Data were gridded into the Albers Equal-Area Conic (AEAC) projection.	proprietary
 relampagolma_1	Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) Lightning Mapping Array (LMA) V1	GHRC_DAAC STAC Catalog	2018-11-08	2019-04-20	-66.166, -33.464, -61.959, -29.856	https://cmr.earthdata.nasa.gov/search/concepts/C1979892577-GHRC_DAAC.umm_json	The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) Lightning Mapping Array (LMA) was an 11-station, ground-based network located in north-central Argentina from November 2018 to April 2019 in support of the RELAMPAGO field campaign. The RELAMPAGO campaign aimed to characterize the atmospheric conditions and terrain effects that facilitate the initiation and growth of intense weather systems in this region of South America. The LMA maps Very High Frequency (VHF) emissions from lightning in three dimensions. These emissions have also been grouped, temporally and spatially, into individual flashes, and the flash characteristics analyzed to produce gridded products. The dataset was produced by NASA Marshall Space Flight Center (MSFC), via an agreement with the National Oceanic and Atmospheric Administration (NOAA), in order to serve as a validation dataset for the Geostationary Lightning Mapper (GLM). These LMA data are available from November 8, 2018 through April 20, 2019 in ASCII, HDF5, and netCDF-4 format.	proprietary
-rema-topography-and-antarcticalc2000-for-wrf_1.0	REMA topography and AntarcticaLC2000 for WRF	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	180, -90, -180, -58	https://cmr.earthdata.nasa.gov/search/concepts/C2789817063-ENVIDAT.umm_json	Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data  are now available as static data input for the Weather Research and Forecasting model (WRF). Topography  and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods  applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves  the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.	proprietary
-reproducibility-dataset-for-cryowrf-validation_1.0	Reproducibility dataset for CRYOWRF validation	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, -60	https://cmr.earthdata.nasa.gov/search/concepts/C3226082844-ENVIDAT.umm_json	"This dataset contains data and scripts for ""CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB"" (Gerber et al., submitted).    * Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.  * Static_input: Static topography input file of WRF (geo_em.d01).   * WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.  * WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.  * Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.    Note:    * To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.  * AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions."	proprietary
-research-stillberg_1.0	Bibliography of the long-term treeline research site Stillberg, Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.86716, 46.773573, 9.86716, 46.773573	https://cmr.earthdata.nasa.gov/search/concepts/C3226082894-ENVIDAT.umm_json	# Background information    The Stillberg ecological treeline research site in the Swiss Alps was established in 1975, with the aim to develop ecologically, technically, and economically sustainable reforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Over almost fifty years, research at the Stillberg site combined long-term monitoring of the large-scale high-elevation afforestation with experimental manipulations simulating global change impacts. Besides providing a scientific basis and practical guidelines for high-elevation afforestation, this research has contributed to a comprehensive understanding of ecological processes in the treeline ecotone across different compartments and scales, from individual trees, non-tree vegetation and soils to whole ecosystems, in the context of global change resulting in more than 150 publications.     # Dataset generation    We compiled a comprehensive list of scientific publications covering research at the Stillberg research site by conducting searches in the literature databases Web of Science and Google Scholar, as well as in the Digital Object Repository of the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (DORA). We compiled all publications about the afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, the G-TREE experiment, as well as other studies related to the Stillberg research site.      # Data description    The Stillberg bibliography (Stillberg_bibliography_data_v1.csv) comprises a comprehensive list of 276 scientific publications, 91 of them published in peer-reviewed ISI journals. Currently the bibliography comprises literature about the main afforestation experiment, the FACE × warming experiment, the nutrient addition experiment, and the G-TREE experiment, as well as further publications related to the Stillberg research site that have been published until August 2023. The bibliography can be filtered for different categories, e.g., experiment, peer-review, source repository or database, and source title. The bibliography is described in a metadata file (Stillberg_bibliography_metadata_v1.csv). The bibliography along with the metadata file are provided in a ZIP-folder (Stillberg_bibliography_v1.zip).	proprietary
-resolution-in-sdms-shapes-plant-multifaceted-diversity_1.0	Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	4.855957, 42.9451645, 17.5561523, 48.4044086	https://cmr.earthdata.nasa.gov/search/concepts/C2789815536-ENVIDAT.umm_json	This dataset comprises a large array of ecological data for the European Alps:        (1) Current soil and climate predictors at various resolutions.      (2) GBIF observations of the European Alps Flora (~4,000 species).      (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)      at various resolutions used in the study to generate (4); except 'expert'...      (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions      (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,      SOIL and CLIM-SOIL models.      (5) Ecological and altitudinal preferences of the European Alps Flora.      (6) Data outputs of the related published article.      (7) All scripts used for analyses.      (8) Additional files used for analyses.      (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations      filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +      non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.	proprietary
-restoring-grassland-multifunctionality_1.0	Restoring grassland multifunctionality	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.4697723, 47.3929884, 8.7128448, 47.4830893	https://cmr.earthdata.nasa.gov/search/concepts/C2789815735-ENVIDAT.umm_json	"Please cite this paper together with the citation for the datafile.    Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.            ### Study area    The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).    ### Experimental design and sampling    A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions.  In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.     Vegetation properties    All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).     ### Aboveground arthropods    Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed"" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.    ### Belowground fauna     Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).  To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.    For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).     We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014).  Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.    ### Soil chemical and physical properties, soil nitrogen mineralization    We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).    Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.    To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).    Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.    ### References    Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.    Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.    Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.    Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.    Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.    Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.    Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.    Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.    Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.    Edgar, R. 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Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.    Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.    Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.    Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.    Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.    Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.    Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.    Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.    Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.    Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331."	proprietary
-rit1_1.0	RIT1: Processed permafrost borehole data (2690  m asl), Ritigraben, Switzerland	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	7.84982, 46.17467, 7.84982, 46.17467	https://cmr.earthdata.nasa.gov/search/concepts/C2789815931-ENVIDAT.umm_json	_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._    Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.	proprietary
-rit2_1.0	RIT2: Meteorological station at Ritigraben borehole site	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	7.84982, 46.17467, 7.84982, 46.17467	https://cmr.earthdata.nasa.gov/search/concepts/C2789816265-ENVIDAT.umm_json	Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.	proprietary
 rivdis_199_1	Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS)	ORNL_CLOUD STAC Catalog	1807-01-01	1991-09-30	-178.5, -47.35, 176.73, 72.12	https://cmr.earthdata.nasa.gov/search/concepts/C2756230785-ORNL_CLOUD.umm_json	The Global Monthly River Discharge Data Set (RivDIS) contains monthly averaged discharge measurements for 1,018 stations located throughout the world from 1807-1991. The period of record varies widely from station to station with a mean of 21.5 years. The data are derived from the published UNESCO archives for river discharge, and checked against information obtained from the Global Runoff Center in Koblenz, Germany through the U.S. National Geophysical Data Center in Boulder, Colorado.	proprietary
 river_carbon_flux_xdeg_1028_1	ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans	ORNL_CLOUD STAC Catalog	1947-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785309136-ORNL_CLOUD.umm_json	The River Carbon Flux data set represents estimates for the riverine export of carbon and of sediments. This data set includes the amounts of carbon and of sediments that are discharged to the oceans by rivers for each coastal grid point which receives river inputs. This data set contains three compressed (*.zip) files: the original data at 2.5 x 2.0 degrees, and global maps at spatial resolutions of 0.5 and 1.0 degree which the ISLSCP II staff has created from the original data. 	proprietary
 river_discharge_cpep_640_1	SAFARI 2000 River Discharge Data (Coe and Olejniczak)	ORNL_CLOUD STAC Catalog	1903-01-01	1999-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788346165-ORNL_CLOUD.umm_json	This data set consists of a southern African subset of the Climate, People, and Environment Program (CPEP) Global River Discharge Data Set. The CPEP global river discharge data set is a compilation of monthly mean discharge data for over 2600 sites worldwide. The period of record is variable, from 3 years to greater than 100.	proprietary
@@ -18536,15 +17909,11 @@ rlc_vegetation_1990_700_1	RLC Vegetative Cover of the Former Soviet Union, 1990
 rlc_world_forest_map_697_1	RLC Generalized Forest Map of the Former Soviet Union, 1-km	ORNL_CLOUD STAC Catalog	1998-01-01	1998-12-31	25, 23.21, 180, 71	https://cmr.earthdata.nasa.gov/search/concepts/C2810671823-ORNL_CLOUD.umm_json	This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.	proprietary
 robinson_adelie_colonies_1	Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07	AU_AADC STAC Catalog	2005-09-30	2007-03-31	63.233334, -67.51667, 63.85, -67.36667	https://cmr.earthdata.nasa.gov/search/concepts/C1214311232-AU_AADC.umm_json	GPS surveys of Adelie Penguin colonies in the Robinson Group, Antarctica were carried out by field biologists from the Australian Antarctic Division during the 2005/06 and 2006/07 seasons. In 2005/06 point data were collected representing the presence or absence of colonies on the islands. The data were collected by Matt Low and Lisa Meyer. In 2006/07 polygon data were collected representing the colonies on the islands. The data were collected by Matt Low and Rhonda Pike. The biologists were working on a project led by Dr Colin Southwell of the Australian Antarctic Division.	proprietary
 rock_samples_1	Compilation of Rock Samples collected by ANARE	AU_AADC STAC Catalog	1954-02-01	1999-11-22	60, -75, 160, -35	https://cmr.earthdata.nasa.gov/search/concepts/C1214313719-AU_AADC.umm_json	Rocks from Australian Antarctic Division library  This collection turns out to be rather interesting with some of heritage significance.  Box 1 is basically odds and ends but includes a bag of coal from the Prince Charles Mountains worthy of display.  Boxes 2 and 3 probably all are Phil Law collections.  Unfortunately, locality information generally is lacking, but there are some interesting rocks.  Box 1. A.Loose samples Two pale grey, rounded specimens, one with round depression.  Very light weight (low density).  Probably diatomite or radiolarite.  Source?  Dark grey with some red colours.  Fragment of rounded river pebble that has been broken.  Very tough, either quartzite or volcanic rock.  Source?  Scallop (Pecten meridionalis), left valve  Tasmania  Pink and yellow chert, varnished.  One part of outside looks as if it has been fossil wood.  Could be recrystallised chert from fossil wood locality. Source? Could be Tasmanian.  Two small, dark, angular specimens, quite coarse grained with obvious crystal faces that flash.  Specimens are of quartz and galena (PbS).  Source?  Could be west coast Tasmania such as Zeehan.  Three elongate specimens, pale yellow/off white.  They fit together to produce original specimen about 20 cm long.  These are quite common around coastal Australia where rain soaks through sand, dissolves CaCO3 from surface shell material and redeposits it on the way down, perhaps along the roots of a plant.  Goes by various names such as 'fossil roots' (which is wrong), travertine  Large lump of black glass.  Probably furnace slag but could conceivably be volcanic glass (probably too high density for that).  Vesicles (gas bubbles quite common).  B. Sample bag A calico bag of Permian coal from the Prince Charles Mountains.  Bag is labelled to Assistant Director Science but probably was given to Evlyn Barrett as there is a note inside it suggesting that it is a present.  Some specimens are good and could be used for display.  Box 2. A note in the box (from me to Knowles Kerry) notes that these rocks were collected by Phil Law.  While some cards are there, they are not related to the rocks.  Most would appear to be Antarctic.  Sample with cellotape, labelled Cape North.  Fragment of vein quartz.  Pumice.  Grey, very light weight.  Floats.  Product of March 1962 submarine eruption at Protector Shoal in South Sandwich Islands.  Rafts of this pumice circulated around Southern Hemisphere for years, slowly disappearing as the material became dispersed, washed onto beaches (small fragments still common on Australian beaches and some on Heard Island) and as fragments rubbed together, ground small chips off and these sank.  This sample has some flow structure in it from the original eruption and due to elongation of gas bubbles as it flowed and cooled.  It may well be from Heard Island. &It is identical in composition to material collected by Dr Jon Stephenson in 1963 from 'flotsam north of Heard Island' collected during his period on the latter expedition (Stephenson 1964) and identified as having been derived from vast rafts of pumice released in the South Atlantic Ocean during the eruption in the South Sandwich Islands area in 1962 (Gass et al.. 1963).  This is probably the same material referred to by Dr Phil Law, who commented (personal communication, 19 August 1993) that he had seen rafts of pumice near Heard Island in January 1963.& (quote from Quilty and Wheller in preparation for Heard Island symposium of 1998).  Flat dark grey fragment about 1 cm thick.  Otherwise triangular with sharp corners.  Rock is phyllite, rather low grade metamorphic rock, originally a shale in which clay has changed to muscovite to generate the good cleavage. Source?  Would like to know because I have identical material as a glacial erratic from Kerguelen Plateau.  'Granite' Two fragments - angular, one rounded - of grey granite.  Good samples.  They are not quite the same material.  Angular specimen is probably strictly granodiorite (the difference is important only to geologists).  It contains quartz (very pale grey, glassy), two white feldspars (plagioclase-Na-CaAlsSi3O8 - and orthoclase - KalSi3O8) which make up the bulk of the rock in roughly equal proportions and come in two grain sizes - coarse (about 1 cm) and finer (about 2 mm).   Dark minerals are biotite (black mica) and hornblende (complex Fe/Mg silicate). Rounded specimen is more uniform in grain and probably has the same pale minerals but they are not so easy to identify.  Dark mineral hornblende.  Biotite not seen.  There also is a brown mineral, sometimes rhomboid in cross section.  This probably is sphene.  Source of samples?  Rauer Island Rocks.  (Probably Phil Law's own labelling) Replaced in old plastic bag and in turn in a new thin one.  Two glassy (vitreous) grey samples.  Monominerallic.  Vein quartz.  Two flat specimens with marked orientation of very uniform grained constituent minerals.  Both high grade metamorphic rocks - amphibolite gneiss.  Mineralogy - quartz, amphibole (probably hornblende), plagioclase feldspar.  In one the quartz is white and in the other, more yellowish.  Rounded specimen with two rock types in it with clear boundary.  Pale rock is quartzite and other is amphibolite, probably part of same sequence as other amphibolites.  Other rock has great variation in grain size but is otherwise part of the same sequence.  Darker part is amphibolite, coarser than in samples described above and with yellowish quartz and orthoclase.  This rock seems to be the source of the sand grains as it is more friable than others.  Garnet rich sample - Bag 1 One rounded sample contains a significant content of garnet in white 'matrix'.  The pale material is quartz/orthoclase and there is a fine grained, high lustre black mineral that could be magnetite (Fe3 O4).  Source??? Probably a Law sample.  Three specimens in small bag - Bag 2 All are characterised by having quartz veins 1-1.5 cm thick, cutting across the sample and bounded by a layer 1-2 mm thick of a black mineral (amphibole, probably hornblende).  Other constituents of the rock are yellowish quartz, traces of garnet and biotite.  I couldn't identify any feldspar but would expect it.  The rocks, although not labelled with a locality, are very similar to some of those described as from the Rauer Islands but there are some in the Vestfold Hills that are very similar.  Metabasalt? - Bag 4 - two samples These look rather like the basalt dykes that are so characteristic of the Vestfold Hills but are they? And who collected them?  They probably are Phil Law collections.  The dykes were intruded in a series of about 9 episodes from about 2.2 billion to 1.1 billion years.  They have been altered since intrusion and while bulk composition changed little, the mineralogy did.  They are now very tough rocks that break with highly angular, brittle fractures.  Box 3 Judging by the brown sample bag, I suspect these are also Phil Law collections but where from?  Brown calico bag - 5 specimens Large specimen is amphibolite gneiss consisting of layers that are amphibole and biotite rich.  Also has traces of garnet.  Locality?  Two pale specimens.  Both contain prominent garnet in quartz-feldspar matrix, orthoclase dominating.  Metamorphic.  Locality?  Two small specimens.  One is coarser than the other and has obvious garnet with hornblende, biotite, quartz and feldspar.  The other is mainly hornblende/quartz but is a surface specimen, somewhat weathered.  Brown paper bag (now in plastic bag - 5) Small sample (two almost black specimens).  These are different from anything noted above.  While the black biotite is the dominant source of the colour, there is also some quartz and I suspect feldspar.  There also is quite a deal of very fine acicular mineral.   It could be one of several but sillimanite (one of several minerals with the formula Al2SIO3) is a possibility.  Largest, dark sample.  Amphibolite gneiss.  Well banded.  Pale bands of quartz-feldspar-muscovite (white mica).  Dark bands of hornblende-biotite. Source???  Dominantly pale sample with dark patch.  Pale part is quartz-feldspar and the dark is hornblende plus minor acicular mineral (sillimanite?).  Thin sample, 6 x 5 cm, 4 mm thick.  Details not clear.  Too fine grained but probably mainly quartz-feldspar with minor dark mineral (hornblende?).  Plastic bag 6. Large flat specimen and one chip off the large block.  Low grade metamorphic rock, originally fine sandstone. Source?  Plastic bag 7 Rock mainly of coarse K-feldspar and quartz with minor plagioclase.  Rock includes layers of brown mica (phlogopite?).  Metamorphic.  Source?  Plastic bag 8. 8A. 3 specimens (2 are counterparts).  See also 'Brown paper bag' sample above.   Biotite-quartz-sillimanite. 8B. 2 specimens.  Beautiful banded gneiss.  Bands are pale, dominantly quartz and dark, dominantly biotite with some hornblende. 8C. 2 specimens. Quartz-biotite schist with trace of acicular mineral (sillimanite?) and pyrite.  Two remaining specimens. One is of quartz/feldspar(?)/biotite/hornblende-sillimanite?  Is feldspar correctly identified?  Sieve texture.  Other is subrounded boulder, greenish (chlorite?). Patrick G. Quilty AM 22 November 1999	proprietary
-rockfall-gallery-testing-parde-2016_1.0	Rockfall gallery testing Parde 2016	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	8.698082, 46.6532196, 8.698082, 46.6532196	https://cmr.earthdata.nasa.gov/search/concepts/C2789816316-ENVIDAT.umm_json	"Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. ""Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie"", WSL-Berichte, Heft 68, 2018."	proprietary
-root-traits_1.0	Root-traits	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.6130533, 46.3023351, 7.6130533, 46.3023351	https://cmr.earthdata.nasa.gov/search/concepts/C2789816345-ENVIDAT.umm_json	Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald    Data_Fig.1.xlsx  Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring    Data_Tab1+2_2005.xlsx  Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest    Data_Tab1+2_2016.xlsx  Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest	proprietary
 root_biomass_658_1	Global Distribution of Fine Root Biomass in Terrestrial Ecosystems	ORNL_CLOUD STAC Catalog	1965-01-01	1996-12-31	-160, -54, 175, 75	https://cmr.earthdata.nasa.gov/search/concepts/C2808094112-ORNL_CLOUD.umm_json	A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.	proprietary
-root_mass_of_live_trees_zell_wutzler-210_1.0	Root mass of live trees (Zell, Wutzler)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816330-ENVIDAT.umm_json	Dry weight (mass) of the belowground part (roots) of living trees and shrubs  starting at 12 cm dbh. The dimensions of the roots are determined according to Zell and Wutzler.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 root_nutrients_659_1	Global Distribution of Root Nutrient Concentrations in Terrestrial Ecosystems	ORNL_CLOUD STAC Catalog	1959-01-01	1997-01-01	-127.83, -35.35, 148.9, 60.82	https://cmr.earthdata.nasa.gov/search/concepts/C2761763711-ORNL_CLOUD.umm_json	Nutrient measurements for fine roots were compiled from 56 published studies providing information on 372 different combinations of species, root diameter, rooting depths, and soils at a variety of locations. The compilation was used to examine dynamics of 14 nutrients, including translocation properties of roots of varying size and status.	proprietary
 root_profiles_660_1	Global Distribution of Root Profiles in Terrestrial Ecosystems	ORNL_CLOUD STAC Catalog	1925-01-01	2000-12-31	-157, -46, 176, 76	https://cmr.earthdata.nasa.gov/search/concepts/C2761763803-ORNL_CLOUD.umm_json	Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.	proprietary
 root_turnover_661_1	Global Distribution of Root Turnover in Terrestrial Ecosystems	ORNL_CLOUD STAC Catalog	1946-01-01	1999-12-01	-160, -54, 175, 75	https://cmr.earthdata.nasa.gov/search/concepts/C2761764652-ORNL_CLOUD.umm_json	Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature.  Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide.	proprietary
 root_water_storage_1deg_1006_1	ISLSCP II Total Plant-Available Soil Water Storage Capacity of the Rooting Zone	ORNL_CLOUD STAC Catalog	1987-01-01	1988-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785281172-ORNL_CLOUD.umm_json	"This data set provides two estimates of the geographic distribution of the total plant-available soil water storage capacity of the rooting zone (""rooting zone water storage size"") on a 1.0 degree global grid. Two inverse modeling methods were used. The first modeling approach (optimization) was based on the assumption that vegetation has adapted to the environment such that it makes optimum use of water (Kleidon and Heimann 1998). The second method (assimilation) was based on the assumption that green vegetation indicates sufficient available water for transpiration (Knorr 1997). The data set was developed to provide alternative means to describe rooting characteristics of the global vegetation cover for land surface and climate models in support of the ISLSCP Initiative II data collection. There are three files in this data set. "	proprietary
-ros_data_1.0	Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816385-ENVIDAT.umm_json	"Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of ""virtual meteorological stations"" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes."	proprietary
 rs15bmlc_483_1	BOREAS RSS-15 SIR-C and TM Biomass and Landcover Maps of the NSA and SSA	ORNL_CLOUD STAC Catalog	1994-04-13	1995-09-02	-105.61, 53.63, -97.75, 56.21	https://cmr.earthdata.nasa.gov/search/concepts/C2929140955-ORNL_CLOUD.umm_json	The RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape.	proprietary
 rs16cm61_563_1	BOREAS RSS-16 AIRSAR CM V6.1 Images	ORNL_CLOUD STAC Catalog	1993-08-12	1995-07-31	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929173275-ORNL_CLOUD.umm_json	Satellite and aircraft SAR data used in conjunction with ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV).	proprietary
 rs17diel_301_1	BOREAS RSS-17 Dielectric Constant Profile Measurements	ORNL_CLOUD STAC Catalog	1994-04-19	1994-04-28	-106.2, 53.63, -98.29, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2807645232-ORNL_CLOUD.umm_json	Contains dielectric profile  measurements taken by RSS-17 at NSA and SSA treed tower sites.	proprietary
@@ -18612,14 +17981,6 @@ s2k_soller_wetlands_635_1	SAFARI 2000 Freshwater Wetlands, 1-Deg (Stillwell-Soll
 s2k_zinke_soil_638_1	SAFARI 2000 Organic Soil Carbon and Nitrogen Data (Zinke et al.)	ORNL_CLOUD STAC Catalog	1940-01-01	1984-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2804802106-ORNL_CLOUD.umm_json	This data set contains a subset of the Worldwide Organic Soil Carbon and Nitrogen (Zinke et al. 1986) data set for southern Africa. The data were obtained from soil surveys by Zinke and soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations.&	proprietary
 sabor_0	Ship-Aircraft Bio-Optical Research (SABOR) collaborative campaign	OB_DAAC STAC Catalog	2014-07-18		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360635-OB_DAAC.umm_json	SABOR (Ship-Aircraft Bio-Optical Research) Collaborative campaign occurred from 19 Jul 2014 - 05 Aug 2014 that sampled the Gulf of Maine, near Bermuda, and shore-waters off the East coast of the United States.	proprietary
 sage_685_1	LBA Regional River Discharge Data (Coe and Olejniczak)	ORNL_CLOUD STAC Catalog	1903-01-01	1999-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2777328159-ORNL_CLOUD.umm_json	"This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W).The global river discharge data set (Coe and Olejniczak 1999), formerly known as the ""Climate, People, and Environment Program (CPEP) Global River Discharge Database,"" is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html."	proprietary
-sagehen_cycles_1.0	Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	-120.3847504, 39.3955455, -120.2007294, 39.464493	https://cmr.earthdata.nasa.gov/search/concepts/C2789816538-ENVIDAT.umm_json	Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.    Data include 30-minute time series of     - weather variables,     - sap flow fluxes,     - groundwater levels (in two riparian transects of shallow groundwater wells),     - and stream stages (at 12 sites spanning a 500-meter elevation gradient),    and daily time series of    - temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations    - diel cycle index values for groundwater levels and stream stages,     - and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.    Google Earth Engine scripts for extracting the MODIS data are also provided.	proprietary
-saltation-of-cohesive-granular-materials_1.0	Saltation of cohesive granular materials	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816677-ENVIDAT.umm_json	The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.     The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.    In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.	proprietary
-salvage_logging-27_1.0	Salvage logging	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816815-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest due to damage occurring (e.g. windthrow, avalanches, insects or rockfall), and not as the result of management planning. This feature is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-salvage_logging_due_to_insects-89_1.0	Salvage logging due to insects	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816975-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case insects, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-salvage_logging_due_to_insects_star-251_1.0	Salvage logging due to insects*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816611-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case insects, and not because of management planning. This feature is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-salvage_logging_due_to_wind-88_1.0	Salvage logging due to wind	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816712-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case windthrow, and not due to silvicultural planning. This theme is derived on the level of a sample plot  from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).   The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-salvage_logging_due_to_wind_star-250_1.0	Salvage logging due to wind*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816852-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case windthrow, and not because of management planning. This theme is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-salvage_logging_star-186_1.0	Salvage logging*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816970-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were removed from the forest as a result of damage occurring (e.g. windthrow, avalanches, insects, rockfall), and not because of management planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 samsa94d_462_1	BOREAS/SRC AMS Suite A Surface Meteorological and Radiation Data: 1994	ORNL_CLOUD STAC Catalog	1993-12-15	1994-12-31	-108.51, 52.15, -97.87, 56.89	https://cmr.earthdata.nasa.gov/search/concepts/C2813521188-ORNL_CLOUD.umm_json	Contains the data collected in 1994 by the AMS suite A instrument set operated by SRC and provided to BORIS.	proprietary
 samsa95d_463_1	BOREAS/SRC AMS Suite A Surface Meteorological and Radiation Data: 1995	ORNL_CLOUD STAC Catalog	1995-01-01	1995-12-31	-108.51, 52.15, -97.87, 56.89	https://cmr.earthdata.nasa.gov/search/concepts/C2929131838-ORNL_CLOUD.umm_json	Contains the data collected in 1995 by the AMS suite A instrument set operated by SRC and provided to BORIS.	proprietary
 samsa96d_464_1	BOREAS/SRC AMS Suite A Surface Meteorological and Radiation Data: 1996	ORNL_CLOUD STAC Catalog	1996-01-01	1996-12-31	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2813524206-ORNL_CLOUD.umm_json	Contains the data collected in 1996 by the AMS suite A instrument set operated by SRC and provided to BORIS.  	proprietary
@@ -18630,7 +17991,6 @@ sar_subsets_993_1	SAR Subsets for Selected Field Sites, 2007-2010	ORNL_CLOUD STA
 saskfc1m_510_1	BOREAS SERM Forest Cover Data of Saskatchewan in Vector Format	ORNL_CLOUD STAC Catalog	1980-01-01	1989-12-31	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2929157906-ORNL_CLOUD.umm_json	A condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU).  Map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile).  The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base.	proprietary
 saskffcc_307_1	BOREAS Saskatchewan Forest Fire Control Centre Surface Meteorological Data	ORNL_CLOUD STAC Catalog	1995-05-05	1995-10-01	-108.42, 53.33, -102.48, 55.38	https://cmr.earthdata.nasa.gov/search/concepts/C2807645802-ORNL_CLOUD.umm_json	Contains 1994 and 1995 hourly data from various forestry meteorology stations.	proprietary
 saskfire_308_1	BOREAS SERM Forest Fire Chronology of Saskatchewan in Vector Format	ORNL_CLOUD STAC Catalog	1945-01-01	1996-12-31	-110, 49, -101.6, 60	https://cmr.earthdata.nasa.gov/search/concepts/C2846961544-ORNL_CLOUD.umm_json	Series of ARC/INFO export files of the fire history of Saskatchewan by year from 1945 to 1996, with a few missing years.	proprietary
-satellite-avalanche-mapping-validation_1.0	Satellite avalanche mapping validation data	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.6837616, 46.6742944, 9.9694061, 46.8727491	https://cmr.earthdata.nasa.gov/search/concepts/C2789817082-ENVIDAT.umm_json	Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).    Coordinate system: CH1903+_LV95    The generation of this dataset is described in detail in:    Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.	proprietary
 sbuceilimpacts_1	SBU Ceilometers IMPACTS	GHRC_DAAC STAC Catalog	2020-01-01	2023-03-02	-73.1278305, 40.8967056, -73.0296555, 40.9652695	https://cmr.earthdata.nasa.gov/search/concepts/C1995869065-GHRC_DAAC.umm_json	The SBU Ceilometers IMPACTS dataset includes ceilometer cloud height measurements collected by the Vaisala CL51, Vaisala CT25K, and Lufft Ceilometer CHM 15k ceilometers operated by the State University of New York (SUNY) Stony Brook University. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands;  and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The ceilometer dataset files are available from January 1, 2020, through March 2, 2023, in netCDF-3 and netCDF-4 formats.	proprietary
 sbukasprimpacts_1	SBU Ka-band Scanning Polarimetric Radar (KASPR) IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-06	2020-02-26	-73.1284, 40.8898, -73.1276, 40.8906	https://cmr.earthdata.nasa.gov/search/concepts/C1995869315-GHRC_DAAC.umm_json	The SBU Ka-band Scanning Polarimetric Radar (KASPR) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) Ka-band Scanning Polarimetric Radar (KASPR) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. KASPR provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 6, 2020 through February 26, 2020 in netCDF-4 format.	proprietary
 sbulidarimpacts_1	SBU Doppler LiDAR IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-01	2020-02-26	-72.8909, 40.8611, -72.8631, 40.8889	https://cmr.earthdata.nasa.gov/search/concepts/C1995869498-GHRC_DAAC.umm_json	The SBU Doppler LiDAR IMPACTS dataset consists of Doppler velocity and backscatter intensity from the Stony Brook University (SBU) Doppler LiDAR. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in netCDF-4 format from January 1 through February 26, 2020.	proprietary
@@ -18652,10 +18012,7 @@ scarmarbin_1807_Not provided	Admiralty Bay Benthos Diversity Data Base (ABBED).
 scarmarbin_1808_Not provided	Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997).	SCIOPS STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json	Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). 	proprietary
 scarmarbin_987_Not provided	A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection)	SCIOPS STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json	The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database.	proprietary
 scarmarbin_ABBED_Not provided	Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN]	SCIOPS STAC Catalog	1906-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json	Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski  (Poland, since 1977), and Ferraz  (Brazil, since 1984).  Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium.  ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal.  Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research.  The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design.	proprietary
-schweizerisches-landesforstinventar-2009-2017_1.0	Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.umm_json	Swiss National Forest Inventory. Results of the fourth survey 2009–2017.  The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings.  Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland    Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017.  In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt,   im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate über den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erläutert. Der Bericht ist thematisch strukturiert nach den europäischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalität, Holzproduktion, biologische Vielfalt, Schutzwald und Sozioökonomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab.  Keywords: Waldfläche, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversität, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz    Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf.	proprietary
-scolytidae_1.0	Scolytidae	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817304-ENVIDAT.umm_json	Scolytidae data from all historic up to the recent projects (29.10.2019) of WSL, collected with various methods in forests of different types.  Data are provided on request to contact person against bilateral agreement.	proprietary
 scrxsondecpexaw_1	St. Croix Radiosondes CPEX-AW V1	GHRC_DAAC STAC Catalog	2021-08-19	2021-09-14	-65.2209, 17.4441, -64.6749, 18.0047	https://cmr.earthdata.nasa.gov/search/concepts/C2418992215-GHRC_DAAC.umm_json	The St. Croix Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 19, 2021 through September 14, 2021 in netCDF and ASCII formats, with associated browse imagery in PNG format.	proprietary
-sdm-env-layers-gdplants_1.0	Environmental layers for SDM simulations (GDPlants)	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789817447-ENVIDAT.umm_json	The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).	proprietary
 sea_elephant_biology_1951_1	Biology of the Sea Elephant (Elephant Seal), Heard Island, 1951	AU_AADC STAC Catalog	1951-08-29	1951-10-31	73.24448, -53.19945, 73.83911, -52.95857	https://cmr.earthdata.nasa.gov/search/concepts/C1214311283-AU_AADC.umm_json	This is a copy of a scanned document which contains a report, as well as tabulated data compiled by K. Brown on Sea Elephants (Elephant Seals) at Heard Island in 1951.  The data are biological in nature, and deal with:  Breeding Season 1951   Formation of the Harems   Arrival of the Bulls   Arrival of the Cows   Birth of the Pups   Lactation   Moult   Pup Mortality   Fertilisation of the Cows   Break up of the Harems   Arrival of the Adolescents	proprietary
 sea_ice_extent_gis_1	Extents of Antarctic sea ice - GIS data - 1973-1999	AU_AADC STAC Catalog	1973-01-18	1999-12-31	-180, -70, 180, -50	https://cmr.earthdata.nasa.gov/search/concepts/C1214311285-AU_AADC.umm_json	This dataset represents extents of Antarctic sea ice derived from passive microwave data.  It includes: maximum and minimum sea ice extent based on 1989 - 99 data; maximum sea ice extent by month for the period October - March based on 1973 - 98 data; mean sea ice extent by month based on 1973 - 1998 data; and maximum sea ice extent averaged over the period 1987 - 1998.  The data referenced by this metadata record has been sourced from another metadata record in this catalogue.  For more information on the dataset see:  Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice [climate_sea_ice].	proprietary
 sea_ice_extent_xdeg_981_1	ISLSCP II Global Sea Ice Concentration	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784896705-ORNL_CLOUD.umm_json	This International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data set, ISLSCP II Global Sea Ice Concentration, is based on the Goddard Space Flight Center (GSFC) Sea Ice Concentrations from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and the Defense Meteorological Satellites Program (DMSP) Special Sensor Microwave/Imager (SSM/I) Passive Microwave Data. This data set contains four zip files which includes sea ice concentration (in percentage of ocean area covered by sea ice), table data and map data.  These original data were re-gridded by the National Snow and Ice Data Center (NSIDC) from their original 25-km spatial resolution and EASE-Grid into equal angle Earth grids with quarter, half and one degree spatial resolutions in latitude/longitude. The ISLSCP II staff have taken the one degree resolution original data provided by the Principal Investigator and created global maps of monthly sea ice concentration on a global one degree grid using the latitude and longitude coordinates that were provided. Individual monthly files were created and written to the ASCII format. The re-gridded one degree original data were also adjusted to match the one degree ISLSCP II land/water mask. 	proprietary
@@ -18665,74 +18022,31 @@ sea_surface_temp_1deg_980_1	ISLSCP II Sea Surface Temperature	ORNL_CLOUD STAC Ca
 seaflux_1	SeaFlux Data Products V1	GHRC_DAAC STAC Catalog	1988-01-01	2018-12-31	-179.87, -85.549, 179.87, 85.549	https://cmr.earthdata.nasa.gov/search/concepts/C1995869798-GHRC_DAAC.umm_json	The SeaFlux Data Products dataset consists of estimates of ocean surface latent and sensible heat fluxes, 2m and 10m wind speed, 2m and 10m air temperature, 2m and 10m air humidity, and skin sea surface temperature. This data product was created by using the SeaFlux V3 model. These data are available globally from January 1, 1988 through December 31, 2018 in netCDF-4 format.	proprietary
 seaice_icecores_nelladan_1985_1	Icecores from Sea Ice, Nella Dan, 1985	AU_AADC STAC Catalog	1985-10-27	1985-11-03	50.1, -66.1, 63, -62.4	https://cmr.earthdata.nasa.gov/search/concepts/C1214311287-AU_AADC.umm_json	During voyage 1 of 1985, sixteen ice cores were drilled from sea ice. Details from those cores include the position they were drilled, length of the core, percentage of the core that was frazil ice, and comments on the state of the core, or observations of the ice make-up.  Physical records are archived at the Australian Antarctic Division.	proprietary
 seamap47_Not provided	Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment	SCIOPS STAC Catalog	1994-06-13	1997-11-22	-124.81862, 33.78087, -118.39433, 41.182	https://cmr.earthdata.nasa.gov/search/concepts/C1214589846-SCIOPS.umm_json	Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara.              PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz	proprietary
-seasonal-fractional-snow-covered-area-algorithm_1.0	Seasonal fractional snow-covered area algorithm	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817560-ENVIDAT.umm_json	This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.    The current version can be found in the WSL/SLF Gitlab repository:   https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area	proprietary
-seasonal-snow-data-wy-2016-2022_1.0	Seasonal snow data for Switzerland OSHD - FSM2sohd	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083044-ENVIDAT.umm_json	This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.	proprietary
 seawater-temp-casey-Dec03_1	Marine water temperatures around Casey station - December 2003	AU_AADC STAC Catalog	2003-12-01	2004-01-01	110.35217, -66.51326, 110.67627, -66.23146	https://cmr.earthdata.nasa.gov/search/concepts/C1214311249-AU_AADC.umm_json	Water temperatures were recorded by Tidbit temperature loggers attached to experimental mesocosms suspended below the sea ice at four sites around Casey in summer 2003/04. Data are temperature in degrees Celsius automatically logged every 5 minutes between the 01/12/2003 and 31/12/2003 at Brown Bay inner (S66 16.811 E110 32.475) and McGrady Cove (S66 16.556 E110 34.392), and between 02/12/2003 and 01/01/2004 at Brown Bay outer (S66 16.811 E110 32.526) and O'Brien Bay (S66 18.730 E110 30.810). Three loggers were deployed at each site; loggers A and B - one attached to each of two mesocosms (perforated 20 litre food buckets) and another - logger I - attached to plastic tubing approximately 1 metre above the mesocosms. Only two data loggers (A and B) were deployed at Mcgrady Cove. Mesocosms were suspended two to three metres below the bottom edge of the sea ice through a 1 metre diameter hole and were periodically raised to the surface for short periods (~1 hour). This experiment was part of the short-term biomonitoring program for the Thala Valley Tip Clean-up at Casey during summer 2003/04.  These data were collected as part of ASAC project 2201 (ASAC_2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities).  See also other metadata records by Glenn Johnstone for related information.  The fields in this dataset are:  Date Time Temperature Location	proprietary
 seawifs_624_1	SAFARI 2000 SeaWiFS Images for Core Study Sites, 2000-2001	ORNL_CLOUD STAC Catalog	2000-04-12	2001-05-17	8.73, -35.26, 43.2, -7.49	https://cmr.earthdata.nasa.gov/search/concepts/C2804816085-ORNL_CLOUD.umm_json	This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the eight core study sites of Mongu, Etosha, Kasangu, Skukuza, Mutoko, Mzola, Nampula, and Ndola. There are two main sets of local area coverage (LAC) data: Level-1 and Level-2 200-km x 200-km image subsets for seven of the sites and 400-km x 400-km image subsets for the Etosha site. The data are provided in HDF format files.	proprietary
 seawifs_region_625_1	SAFARI 2000 SeaWiFS Images for the Southern African Region, 1999-2001	ORNL_CLOUD STAC Catalog	1999-01-01	2001-03-03	8.73, -35.26, 43.2, -7.49	https://cmr.earthdata.nasa.gov/search/concepts/C2804817543-ORNL_CLOUD.umm_json	This data set contains Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery for the southern African region. These images are Level-1a swaths of the southern African region selected from global area coverage (GAC) at 4.5-km resolution. The data are provided in HDF format files.	proprietary
-secondary-ice-production-processes-in-wintertime-alpine-mixed-phase-clouds_1.0	"Data for the publication ""Secondary ice production processes in wintertime alpine mixed-phase clouds"""	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.983151, 46.548308, 7.983151, 46.548308	https://cmr.earthdata.nasa.gov/search/concepts/C2789817759-ENVIDAT.umm_json	This repository contains all WRF model outputs and observational data sets used for the paper:  Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.	proprietary
-sediment-transport-observations-in-swiss-mountain-streams_1.0	Sediment transport observations in Swiss mountain streams	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	7.1095008, 46.1234183, 9.5849948, 47.0451026	https://cmr.earthdata.nasa.gov/search/concepts/C2789817859-ENVIDAT.umm_json	The Swiss Federal Research Institute WSL has extensive experience with surrogate bedload transport measurements. The first measuring site was established in the Erlenbach stream, a small (first-order) catchment in the pre-alpine valley Alptal in central Switzerland. Continuous bedload transport measurements were started in 1986, using first piezoelectric sensors (1986 to 1999) and then geophone sensors (from 2002 onwards) underneath a steel plate and mounted flush with the streambed. In the meantime, the so-called Swiss plate geophone (SPG) system has been installed at more than 20 field sites, primarily in smaller and steeper streams in Switzerland, Austria, and Italy but also in a few larger rivers and in some other streams worldwide (Israel, USA, Japan).   Sediment transport observations in Switzerland with the SPG system concern the following streams: Erlenbach near Brunni (Alptal valley), Albula at Tiefencastel, Navisence at Zinal, Avançon de Nant near Pont de Nant (see map). The data in this repository primarily refer to calibration measurements with the SPG system. The publications listed here discuss primarily the performance of the measuring system but also process-based aspects of bedload transport.	proprietary
 sediments_gom_Not provided	Gulf of Maine Contaminated Sediments Database	CEOS_EXTRA STAC Catalog	1970-01-01		-71.4661, 40.6306, -67.2693, 44.6999	https://cmr.earthdata.nasa.gov/search/concepts/C2231553179-CEOS_EXTRA.umm_json	The overall objective of this project is to create a database of existing data on contaminants in sediment for the Gulf of Maine region that will be useful to persons throughout the region for scientific and management purposes. This task involves identification of data sources, entry of data into the database format, validation or scientific editing of the database, some analysis and synthesis of the compiled data, and publication of the database and associated bibliographies. The tasks of locating and entering data are being shared among the principle investigators in this project because they require a thorough knowledge of the geographic regions under consideration, an understanding of the types of data identified, and familiarity with active research in these regions. This cooperative approach insures that a more thorough identification and collection of data occurs than could take place from one institution. It also insures that the compiled database will be used by all the participants and their colleagues in the future.    Objectives of the work:  1) Develop a comprehensive inventory (database) of available information on sediment contaminants, both inorganic and organic, for the Gulf of Maine  2) Encourage the cooperation and active participation of multiple agencies and organizations in locating, incorporating, and utilizing the data.  3) Place these and ancillary data in interactive, user-friendly, and readily exchangeable forms (such as desktop computer, FTP, and CD-ROM).  4) Map and analyze sediment contaminant distributions in order to provide the best assemblage of information possible for use in determining contaminant baselines  5) Utilize the database to address specific scientific questions about transport and fate of contaminants in the GOM system.  6) Provide guidance for other agencies and organizations to further the usefulness of the data in research, resource management, and public policy decisions.  7) Provide guidance on where to sample and how to analyze samples in the future to make more effective use of limited resources	proprietary
-seilaplan-herunterladen-des-dhm-mit-swissgeodownloader_1.0	Seilaplan Tutorial: DTM download with SwissGeoDownloader	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083074-ENVIDAT.umm_json	In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!      Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader    Link to Seilaplan website: https://seilaplan.wsl.ch      *********************    Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.      Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader    Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch	proprietary
-seilaplan-tutorial-dhm-kacheln-zusammenfugen_1.0	Seilaplan Tutorial: Merge DTM tiles	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083081-ENVIDAT.umm_json	In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!    Link to Seilaplan website: https://seilaplan.wsl.ch      ***************************    Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.    Link zur Seilaplan-Website: https://seilaplan.wsl.ch	proprietary
-seilaplan-tutorial-herunterladen-des-dhm-von-swisstopo-website_1.0	Seilaplan Tutorial: DTM download from swisstopo website	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083089-ENVIDAT.umm_json	In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German!    Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details    Link to the rope map website: https://seilaplan.wsl.ch      ********************    Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.     Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details    Link zur Seilaplan-Website: https://seilaplan.wsl.ch	proprietary
-seilaplan-tutorial-wms-layer-als-hintergrundkarten-laden_1.0	Seilaplan Tutorial: Load WMS layers as background maps	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083098-ENVIDAT.umm_json	In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales,  the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!    Link for the integration of WMS data: https://wms.geo.admin.ch/    Link to the description on the Swisstopo website:  https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html    Link to the Seilaplan website: https://seilaplan.wsl.ch      **************************    Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.       Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/    Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html    Link zur Seilaplan-Website: https://seilaplan.wsl.ch	proprietary
-seilaplan_2.0	Seilaplan	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	4.855957, 43.5878185, 16.1938477, 48.0849294	https://cmr.earthdata.nasa.gov/search/concepts/C2789817891-ENVIDAT.umm_json	Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by  Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).  The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.  The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.  The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.   For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).    License: GNU, General Public License, Version 2 or newer.    Literature:  Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.	proprietary
-selected-wet-snow-avalanche-activity-data-davos-switzerland-2011-2014_1.0	Selected wet snow avalanche activity data Davos, Switzerland (2011-2014)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	9.7084808, 46.6729988, 9.9954987, 46.8658249	https://cmr.earthdata.nasa.gov/search/concepts/C2789817004-ENVIDAT.umm_json	Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both.    The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence.    This dataset is the underlying dataset to:  Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.	proprietary
-sensitivity-of-modeled-snow-instability_1.0	Sensitivity of modeled snow instability	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.8092461, 46.8295484, 9.8092461, 46.8295484	https://cmr.earthdata.nasa.gov/search/concepts/C2789817141-ENVIDAT.umm_json	We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario.  The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.	proprietary
-sentinel-1-grd-bundle-1_NA	Sentinel-1 - Level-1 - Interferometric Wide Swath Ground Range Detected High Resolution	INPE STAC Catalog	2021-05-01	2024-06-17	-76.546547, -35.235916, -31.785385, 6.970906	https://cmr.earthdata.nasa.gov/search/concepts/C3108204188-INPE.umm_json	Copernicus Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. This dataset contains interferometric wide swath ground range detected high resolution data available over Brazil.	proprietary
-sentinel-3-olci-l1-bundle-1_NA	Sentinel-3/OLCI - Level-1B Full Resolution	INPE STAC Catalog	2023-03-04	2024-06-17	-179.431, -45.0723, 179.987, 10.4204	https://cmr.earthdata.nasa.gov/search/concepts/C3108204728-INPE.umm_json	Copernicus Sentinel-3/OLCI Level-1B product OL_1_EFR (EO processing mode for Full Resolution) over Brazil.	proprietary
 shadoz_ozonesonde_726_1	SAFARI 2000 SHADOZ Ozonesonde Data, Zambia and Regional Sites, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-01	2000-11-30	55.48, -7.98, 55.48, -7.98	https://cmr.earthdata.nasa.gov/search/concepts/C2789016629-ORNL_CLOUD.umm_json	Ozonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere. During the campaign, ozonesondes were launched daily during the height of the burning season and in a region of active biomass burning activity.	proprietary
 shirley_dem_1	A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica	AU_AADC STAC Catalog	2005-01-01	2007-05-01	110.473, -66.287, 110.509, -66.277	https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json	This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and   (iv) an orthophoto created using the DEM.  The data are stored in the UTM zone 49 map projection.  The horizontal datum is WGS84.   The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins.  See a related URL below for a map showing Shirley island.	proprietary
 simrad_SO_Not provided	Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC.	SCIOPS STAC Catalog	2002-08-03	2002-09-15	-75.5, -68.75, -69.5, -65.75	https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json	Using the hull mounted Simrad EK500 Scientific Sounder System,             acoustic returns from 38, 120, and 200 kHz transducers were recorded             continuously along ship's track from Aug 3 - Sept 15, 2002.  Of             interest, was the acoustic returns from zooplankton patches and             density structures, and the signel correlations with known plankton             tows and CTD casts.  The survey area included the continental margin             to the west of the Antarctic Peninsula extending from the northern tip             of Adelaide Island to the southern portion of Alexander Island,             Crystal Sound, and Marguerite Bay.  These data have been reduced to             daily files and are supported by software for manipulative purposes.          Ship name/cruise ID/dates of cruise             RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002	proprietary
-simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0	Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	9.80934, 46.82962, 9.80934, 46.82962	https://cmr.earthdata.nasa.gov/search/concepts/C2789817408-ENVIDAT.umm_json	Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.  Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.	proprietary
-simulated-future-discharge-and-climatological-variables_1.0	Simulated future discharge and climatological variables for medium-sized catchments in Switzerland	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817564-ENVIDAT.umm_json	"Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).  The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX  for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:    - ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11  - ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11    - ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11    - ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11    - ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11    - ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11    - MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11    - MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11    - MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11    - MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11      __*References*__:    -	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.  -	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.  -	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.  -	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH.  Part II - Physical model description_, Geographica Bernensia, 40, 1-89.  -	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.  -	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation  for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022."	proprietary
-simulating-chamois-populations_1.0	Simulating population divergence of Northern chamois in the Alps based on habitat dynamics	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	4.8, 43.5, 16.3, 48.3	https://cmr.earthdata.nasa.gov/search/concepts/C2789817711-ENVIDAT.umm_json	# General description    Genomic data, habitat suitability raster files and scripts to run gen3sis to simulate cumulative divergence over time as approximation for genetic differentiation. Scripts for basic analysis of the simulations (e.g., create distance matrix from sampling locations) are provided, too. See original publication (doi link will be provided after publication) for details. The study area are the European Alps.  All data is uploaded as zipped file. Unzip them after the download and put all data in one folder.  See linked publications for correct citation of the data used, use of the data without correct citation is not allowed.    __Corresponding author__: Flurin Leugger, email: flurin.leugger@gmail.com      # Description of the data (content of the different zip folders)    ## Abiotic data    ### Glaciers  Folders with raster stacks with glaciated areas at 0.05° resolution in WGS84 projection from Seguinot et al. (2018).  Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., & Preusser, F. (2018). Modelling last glacial cycle ice dynamics in the Alps. _The Cryosphere, 12(10)_, 3265–3285. https://doi.org/10.5194/tc-12-3265-2018    ### Rivers  * __river_raster_elevation_class.tif__: raster file (.tif) at 0.05° resolution and WGS84 projection with large rivers (scenario 2 from publication). The rivers (each cell) is classified according to the elevation of the cell.  Natural Earth. (2018). Rivers + lake centerlines version 4.1.0. Retrieved January 22, 2020, from https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines  * __river_raster_strahler_class_5km.tif__: raster file at 0.05° resolution and WGS84 projection with medium rivers. The rivers are classified according to their Strahler order.  Food and Agriculture Organization of the United Nations. (2014). Rivers in Europe (Derived from HydroSHEDS). Retrieved January 29, 2020, from http://www.fao.org/geonetwork/srv/fr/google.kml?uuid=e0243940-e5d9-487c-8102-45180cf1a99f&layers=AQUAMAPS:37253_rivers_europe    ## Fossil records  * __chamois_fossil_combined_public.xlsx__: list with fossil records until 20,000 years BP from Central Europe, see linked references for citation.     ## Chamois occurrences  * __chamois_occurrence.csv__: Chamois presences from all sources used for the publication (see Suppl. mat. Table S1 for detailed information and correct citations of the data) aggregated at 0.05° resolution (~5km).    ## Gen3sis  * __config__: folders with all configuration files used to run the simulations for the publication (different dispersal divergence parameters).  * __scripts__: scripts (and helper functions) to run the gen3sis simulations including scripts for the beginning of the subsequent analysis.    ## Genetic  * __populations.snps.light.vcf__: vcf file of the sampled Northern chamois _(Rupicapra rupicapra)_ . The genomic data encompasses 20k SNPs (from ddRAD sequencing).  * __Sequencing_final_without_slovakia.txt__: sampling locations of Northern chamois _(Rupicapra rupicapra)_    ## HSM  * __habitat_suitability_hindcasting__: Aggregated habitat suitability raster files (stacks, .grd files) at 0.05° resolution and WGS84 projection from 20,000 years BP until today in 100 year time steps. There are separate folders for each environmental variable scenario used (different terrain slope variables) an the different occurrence/pseudo-absence sampling strategy used.  * __ODMAP_LeuggerEtAl__2021-10-25.csv__: ODMAP protocol	proprietary
 sir_c_Not provided	Spaceborne Imaging Radar C-band (SIR-C)	USGS_LTA STAC Catalog	1994-04-09	1994-10-11	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567913-USGS_LTA.umm_json	"Spaceborne Imaging Radar-C (SIR-C) is part of an imaging radar system that was flown on board two Space Shuttle flights (9 - 20 April, 1994 and 30 September - 11 October, 1994). The USGS distributes the C-band (5.8 cm) and L-band (23.5 cm) data. All X-band (3 cm) data is distributed by DLR.  There are several types of products that are derived from the SIR-C data:      Survey Data is intended as a ""quick look"" browse for viewing the areas that were imaged by the SIR-C system. The data consists of a strip image of an entire data swath. Resolution is approximately 100 meters, processed to a 50-meter pixel spacing. Files are distributed via File Transfer Protocol (FTP) download.           Precision (Standard) Data consists of a frame image of a data segment, which represents a processed subset of the data swath. It contains high-resolution multifrequency and multipolarization data. All precision data is in CEOS format.  The following types of precision data products are available:      Single-Look Complex (SLC) consists of one single-look file for each scene, per frequency. Each data segment will cover 50 kilometers along the flight track, and is broken into four processing runs (two L band, two C-band). Resolution and polarization will depend on the mode in which the data was collected. Available as calibrated or uncalibrated data.           Multi-Look Complex (MLC) is based on an averaging of multiple looks, and consists of one file for each scene per frequency. Each data segment will cover 100 km along the flight track, and is broken into two processing runs (one L band and one C band). Polarization will depend on the modes in which the looks were collected. The data is available in 12.5- or 25-meter pixel spacing.           Reformatted Signal Data (RSD) consists of the raw radar signal data only. Each data segment will cover 100 km along the flight track, and the segment will be broken into two processing runs (L-band and C-band).           Interferometry Data consists of experimental multitemporal data that covers the same area. Most data takes were collected during repeat passes within the second flight (days 7, 8, 9, and/or 10). In addition, nine data takes were collected during the second flight that were repeat passes of the first flight. Most data takes were also single polarization, although dual and quad polarization data was also collected on some passes. A Digital Elevation Model (DEM) is not included with any of the SIR-C interferometric data.  The following types of interferometry products are available:      Interferometric Single-Look Complex (iSLC) consists of two or more uncalibrated SLC images that have been processed with the same Doppler centroid to allow interferometric processing. Each frame image covers 50 kilometers along the flight track. The data is available in CEOS format.           Raw Interferogram product (RIn) involves the combination of two data takes over the same area to produce an interferogram for each frequency (L-band and C-band). The data is available in TAR format.           Reformatted Signal Data (RSD) consists of radar signal data that has been processed from two or more data takes over the same area, but the data has not been combined. Although this is not technically an interferometric product, the RSD can then be used to generate an interferogram. Each frame will cover 100 km along the flight track. The data is available in CEOS format."	proprietary
 slgeo_1	SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOTIFF V1	GHRC_DAAC STAC Catalog	2000-09-11	2008-09-08	-91.7794, 27.8502, -82.6518, 31.417	https://cmr.earthdata.nasa.gov/search/concepts/C1979944011-GHRC_DAAC.umm_json	The Sediment Analysis Network for Decision Support (SANDS) Landsat Geotiff dataset includes images for sediment redistribution after a hurricane on the coast of the Gulf of Mexico and then creates a product based on the analysis from September 11, 2000 to September 8, 2008. This dataset consists of the set of daytime GeoTiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis. Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida). These are seasonal data for storms.	proprietary
 slgsa_1	SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOLOGICAL SURVEY OF AL (GSA) ANALYSIS V1	GHRC_DAAC STAC Catalog	2000-09-11	2008-09-08	-90, 27, -84.25, 31	https://cmr.earthdata.nasa.gov/search/concepts/C1979944726-GHRC_DAAC.umm_json	The Sediment Analysis Network for Decision Support (SANDS) Landsat Geological Survey of AL (GSA) Analysis dataset analyzed changes in the coastal shoreline and sedimentation using Landsat GeoTiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project. The daytime GeoTiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area (coordinates 31 to 27 North latitude and 90 to 84.25 West longitude). These are seasonal data for storms from 2001-2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images.	proprietary
-slow-snow-compression_1.0	A grain-size driven transition in the deformation mechanism in slow snow compression	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.8417222, 46.8095077, 9.8417222, 46.8095077	https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.umm_json	We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory.     Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep.       The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.	proprietary
 smart_radiometers_727_1	SAFARI 2000 Surface Atmospheric Radiative Transfer (SMART), Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-15	2000-09-17	31.59, -24.97, 31.59, -24.97	https://cmr.earthdata.nasa.gov/search/concepts/C2789018469-ORNL_CLOUD.umm_json	Surface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena. This allows investigators to participate in coordinated measurement campaigns, such as SAFARI 2000.The SMART instruments were deployed to the Skukuza Airport from August 15 to September 17, 2000 to take part in the SAFARI 2000 Dry Season Aircraft Campaign. The SMART-COMMIT mission is designed to pursue the following goals: Earth Observing System (EOS) validation; innovative investigations; and long-term atmospheric monitoring. The results reported in this data set are for the following instruments deployed and measurements recorded at the Skukuza Airport site within the Kruger National Park: several broadband radiometers, for global, diffuse, direct downward solar irradiance and global infrared downward irradiance; meteorological sensors, for surface air temperature, pressure, relative humidity, and wind; and a Solar Spectral Flux Radiometer (NASA Ames) for spectral solar downward irradiance.	proprietary
 smgeo_1	SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) MODIS GEOTIFF V1	GHRC_DAAC STAC Catalog	2000-09-11	2008-09-09	-90.0021, 27, -84.25, 31.0125	https://cmr.earthdata.nasa.gov/search/concepts/C1979944933-GHRC_DAAC.umm_json	The Sediment Analysis Network for Decision Support (SANDS) MODIS GeoTIFF dataset consists of the set of GeoTIFF images provided to the Geological Survey of Alabama for their analysis. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.	proprietary
 smgsa_1	SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) MODIS GEOLOGICAL SURVEY OF AL (GSA) ANALYSIS V1	GHRC_DAAC STAC Catalog	2000-09-14	2008-09-08	-90, 27, -84.25, 31	https://cmr.earthdata.nasa.gov/search/concepts/C1979946278-GHRC_DAAC.umm_json	The Sediment Analysis Network for Decision Support (SANDS) MODIS Geological Survey of AL (GSA) Analysis dataset consists of geoTIFF images were analyzed for sediment redistribution after hurricanes on the Gulf of Mexico. These are seasonal data for storms from September 14, 2000 to September 8, 2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and/or grids, metadata, and storm temporal information for the sediment analysis images. The Geological Survey of Alabama (GSA) generated this dataset from geoTIFF MODIS images as part of the Sediment Analysis Network for Decision Support (SANDS) project.	proprietary
 smsub_1	SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) MODIS GULF SUBSETTED V1	GHRC_DAAC STAC Catalog	2000-09-11	2008-09-09	-114.391, 23.6847, -60.2855, 34.561	https://cmr.earthdata.nasa.gov/search/concepts/C1979946492-GHRC_DAAC.umm_json	Sediment Analysis Network for Decision Support (SANDS) MODIS Gulf Subsetted dataset consists of daytime images for Terra and Aqua MODIS Reflectance bands 8-16, subsetted to 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida) from September 11, 2000 to September 9, 2008. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.	proprietary
-snow-accumulation-on-arctic-sea-ice-during-mosaic_1.0	snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	53.4375, 84.2418762, 90, 84.9370543	https://cmr.earthdata.nasa.gov/search/concepts/C2789817811-ENVIDAT.umm_json	snowBedFoam 1.0. is a snow transport solver implemented in the computational fluid dynamics software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.   To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physics-based equations similar to the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018 ; Melo et al., 2022). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.  First, snowBedFoam 1.0. was applied to topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021). Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure (Hames et al., 2022).   Second, snowBedFoam 1.0. was used to simulate the snow distribution around the German Antarctic research station Neumayer Station III. The effect of snow properties, fluid forcing and aerodynamic structures on the snow accumulation were assessed.   snowBedFoam 1.0 was implemented in 2 different OpenFOAM versions, namely OpenFOAM-2.3.0 and OpenFOAM-5.0. The latter offers more options for turbulence models and boundary conditions. The fundamental model equations were not changed from one implementation to the other, thus both still correspond to snowBedFoam 1.0. The two branches are called snowBedFoam-v1-2.3.0 (OpenFOAM-2.3.0)  and snowBedFoam-v1-5.0 (OpenFOAM-5.0). The core codes of snowBedFoam 1.0. are directly accessible on the WSL/SLF GitLab repository (more details in the Resources section).	proprietary
-snow-avalanche-data-davos_1.0	Snow avalanche data Davos, Switzerland, 1999-2019	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.7613525, 46.7398606, 9.9563599, 46.8733358	https://cmr.earthdata.nasa.gov/search/concepts/C2789817858-ENVIDAT.umm_json	These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.    This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the   number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related  to avalanche danger level.    The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).  Please refer to the Read-me file for further details on the data.    These data are the basis of the following publication:  Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.	proprietary
-snow-climate-indicators-derived-from-parallel-manuel-snow-measurements_1.0	Snow climate indicators derived from parallel manual snow measurements	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817884-ENVIDAT.umm_json	Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.	proprietary
-snow-deltao18-metamorphism-advection_1.0	Experiments on stable water isotopes,  snow metamorphism, and advection	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.8473656, 46.8125802, 9.8473656, 46.8125802	https://cmr.earthdata.nasa.gov/search/concepts/C2789817929-ENVIDAT.umm_json	Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.   Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.	proprietary
-snow-depth-mapping-by-airplane-photogrammetry-2017-ongoing_1.0	Snow depth mapping by airplane photogrammetry (2017 -  ongoing)	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.7357177, 46.6471776, 10.072174, 46.8466712	https://cmr.earthdata.nasa.gov/search/concepts/C3226083092-ENVIDAT.umm_json	The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.     Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.   The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.   Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.    An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.     The work is published in:	proprietary
-snow-depth-mapping_1.0	Snow Depth Mapping	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.7071075, 46.6838498, 9.9666595, 46.8428318	https://cmr.earthdata.nasa.gov/search/concepts/C2789817957-ENVIDAT.umm_json	The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).  For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the  state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.    The work is published in:     Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015	proprietary
-snow-drift-station-3d-ultrasonic_1.0	Snow Drift Station - 3D Ultrasonic	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.849, 46.859, 9.849, 46.859	https://cmr.earthdata.nasa.gov/search/concepts/C2789818077-ENVIDAT.umm_json	A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s&#8254; &#185;]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .	proprietary
-snow-drift-station-flowcapt_1.0	Snow Drift Station - Flowcapt	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.849, 46.859, 9.849, 46.859	https://cmr.earthdata.nasa.gov/search/concepts/C2789816885-ENVIDAT.umm_json	The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m&#8254; &#178; s&#8254; &#185;) and wind speeds (m s&#8254; &#185;). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.	proprietary
-snow-drift-station-micro-rain-radar_1.0	Snow Drift Station - Micro Rain Radar	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.849, 46.859, 9.849, 46.859	https://cmr.earthdata.nasa.gov/search/concepts/C2789817056-ENVIDAT.umm_json	The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period.  The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).	proprietary
-snow-drift-station-snow-and-air-data_1.0	Snow Drift Station - Snow and Air Data	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.849, 46.859, 9.849, 46.859	https://cmr.earthdata.nasa.gov/search/concepts/C2789817182-ENVIDAT.umm_json	Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.	proprietary
-snow-water-equivalent-for-wagital-catchment-starting-1943_1.0	Snow water equivalent for reference date April 1 for Wägital catchment, starting 1943	ENVIDAT STAC Catalog	2024-01-01	2024-01-01	8.9044619, 47.1065151, 8.9044619, 47.1065151	https://cmr.earthdata.nasa.gov/search/concepts/C2789817839-ENVIDAT.umm_json	Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2024, status 2024-04-22.    Funded currently or in the past by:    - Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland  - Meteodat GmbH  - Institute of Geography, University of Zurich  - WSL Institute for Snow and Avalanche Research SLF  - Institute of Geography, ETH Zurich (IAC ETH Zurich)  - AG Kraftwerk Wägital (AXPO and EWZ)    See also https://www.meteodat.ch/waegital.html	proprietary
 snow_cover_xdeg_982_1	ISLSCP II Northern Hemisphere Monthly Snow Cover Extent	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784896919-ORNL_CLOUD.umm_json	This ISLSCP data set is derived from the National Snow and Ice Data Center (NSIDC) Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent product which combines snow cover and sea ice extent at weekly intervals for October 1978 through June 2001, and snow cover alone from 1966 through June 2001. The original data set was the first representation of combined snow and sea ice measurements derived from satellite observations for the period of record. Designed to facilitate study of Northern Hemisphere seasonal fluctuations of snow cover and sea ice extent, the original NSIDC data set also includes monthly climatologies describing average extent, probability of occurrence, and variance.This data set shows the extent of snow on the land at a variety of scales (1.0 degree, 0.5 degree, 0.25 degree). The values represent the percentage of days in each month where snow was present -- 100 means 100% of the month, 80 means 80% of the month, etc. There are 4 .zip files provided. Missing data is represented by -99 for water and -88 for land. The data were originally in a yearly tabular format. The file was converted to multi-scale maps by plotting each point in the tabular data onto a map of -99 (water) and -88 (land) created from the standard ISLSCP II Land/Sea Mask. 	proprietary
 snowfree_albedo_1deg_956_1	ISLSCP II Monthly Snow-Free Albedo, 1982-1998, and Background Soil Reflectance	ORNL_CLOUD STAC Catalog	1982-01-01	1998-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784881406-ORNL_CLOUD.umm_json	This data set contains monthly average snow-free surface shortwave albedo calculated for the period 1982-1998 and estimates of background soil/litter reflectances in the visible (0.4-0.7 mm) and near-infrared (NIR) (0.7-1.0 mm) wavelengths. Biophysical Parameters derived from the FASIR-NDVI (Fourier Adjusted, Solar zenith angle correction, Interpolation, and Reconstruction of Normalized Difference Vegetation Index) data set developed for the ISLSCP Initiative II data collection for the months of January 1982 through December 1998 were used to calculate monthly mean surface albedos at 1 X 1 degree spatial resolution for vegetated land surfaces (Sellers et al, 1996b) for the wavelength interval from 0.4 to 3.0 mm. The instantaneous albedo is a function of the properties of the land surface and the solar zenith angle. The monthly mean albedo is an average weighted over time weighted by the incident radiation. NDVI data are used to generate the biophysical parameters leaf area index (LAI) and green fraction of vegetation (Greenness) used by the canopy radiative transfer model of the Simple Biosphere (SiB2) model (Sellers et al, 1996a), which computes the instantaneous albedo. This is coupled to the Colorado State University (CSU) General Circulation Model (GCM) (Randall et al, 1989) which integrates the SiB2 radiative transfer through time. The incident radiation for weighting the time-averaged albedo was provided by a previous run of the GCM using the atmospheric radiation parameterization of Harshvardhan et al (1987). The Harshvardhan parameterization models radiative transfer through the atmosphere in both the longwave and shortwave bands, including the effects of cloudiness and water vapor, carbon dioxide and ozone. The shortwave radiation distinguishes between the direct and diffuse components of the solar beam.	proprietary
-snowmeltlysimeter-dataset_1.0	Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.7000024, 47.0288575, 8.7150205, 47.0446816	https://cmr.earthdata.nasa.gov/search/concepts/C2789817311-ENVIDAT.umm_json	This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland.  The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.    The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.	proprietary
-snowmicropen-and-manual-snowpits-from-dronning-maud-land-east-antarctica_1.0	SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	21.62, -72.26, 24.29, -70.46	https://cmr.earthdata.nasa.gov/search/concepts/C2789817468-ENVIDAT.umm_json	SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.	proprietary
-snowmicroquakes_1.0	Compressive stick slip and snow-micro-quakes	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.8472422, 46.8124617, 9.8472422, 46.8124617	https://cmr.earthdata.nasa.gov/search/concepts/C2789817590-ENVIDAT.umm_json	When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature.     The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.	proprietary
-snowmip_1.0	Weissfluhjoch dataset for ESM-SnowMIP	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.809568, 46.829598, 9.809568, 46.829598	https://cmr.earthdata.nasa.gov/search/concepts/C2789817738-ENVIDAT.umm_json	This Weissfluhjoch dataset is a processed version  of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.	proprietary
-soil-fauna-drives-soc-storage-in-a-long-term-irrigated-dry-pine-forest_1.0	Soil fauna drives SOC storage in a long-term irrigated dry pine forest	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817877-ENVIDAT.umm_json	Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris).   Data include: (1) properties of soils sampled in 2011 and 2019 (SOC and N concentrations and stocks, soil masses, 13C and 15N natural abundances, C/N ratios, clay content, pH, inorganic C, stoniness, bulk density); (2) litter mass loss and initial litter chemistry of dominant tree species (Quercus, Pinus, Viburnum) from a litter decomposition experiment carried out in 2014-2015; (3) soil fauna abundance sampled in 2015; (4) soil volumetric water content and soil temperature at 10 cm depth measured during the litter decomposition experiment in 2014-2015; (5) soil mesofauna (Acari and Collembola) diversity and community composition from sampling in 2017; (6) irrigation-induced changes in litterfall (2013-2014, 2016-2017), fine-root production (data 2015 from Brunner et al., 2019, Frontiers in Plant Science), annual soil respiration (estimated for 2014-2015), litter mass loss from litter decomposition experiment (May-October 2014), and SOC stocks measured in 2011 and 2019; (7) Moisture dependency of microbial soil respiration (0-10 cm depth, adapted from Joseph et al., 2020 PNAS), soil respiration measured in 2015 and abundance of Acari, Collembola and Lumbricidae sampled in 2015.	proprietary
-soil-moisture-measurements-davos_1.0	IRKIS Soil moisture measurements Davos	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	9.8297824, 46.7315544, 9.9141504, 46.812365	https://cmr.earthdata.nasa.gov/search/concepts/C2789817893-ENVIDAT.umm_json	Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.    There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:    * vwc_[stn].smet: containing the soil moisture measurements    * station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.    * interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.    At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.    The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.    Please cite the Wever et al. (2017) reference when using this data in publications.    For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.	proprietary
-soil-net-nitrogen-mineralisation-across-global-grasslands_1.0	Soil net nitrogen mineralisation across global grasslands	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	153.6328125, -40.8470604, -140.2734375, 56.2677611	https://cmr.earthdata.nasa.gov/search/concepts/C2789817932-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    Paper Citation:    Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2    Please cite this paper together with the citation for the datafile.    We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/	proprietary
-soil-respiration-exclosure-experiment_1.0	Soil respiration - exclosure experiment	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	10.1273346, 46.6073592, 10.3607941, 46.7580998	https://cmr.earthdata.nasa.gov/search/concepts/C2789817964-ENVIDAT.umm_json	Location of data collection  The Swiss National Park (SNP) is located in the southeastern part of Switzerland, and covers an area of 170 km2, 50 km2 of which is forested, 33 km2 is occupied by alpine and 3 km2 by subalpine grasslands. Elevations range from 1350 to 3170 m a.s.l., and mean annual precipitation and temperature are 871 mm and 0.6°C measured at the Park’s weather station in Buffalora (1980 m a.s.l.) between 1960 and 2009 (MeteoSchweiz 2011). Founded in 1914, the SNP received minimal human disturbance for almost 100 years (no hunting, fishing, or camping, visitors are not allowed to leave the trails). Large (> 1 ha) homogeneous patches of short- and tall-grass vegetation characterize the subalpine grasslands. The average vegetation height of short-grass vegetation is 2 to 5 cm. Red fescue (Festuca rubra L.), quaking grass (Briza media L.) and common bent grass (Agrostis tenuis Sipthrob) are the predominating plant species in this vegetation type. Tussocks of evergreen sedge (Carex sempervirens Vill.) and mat grass (Nardus stricta L.) are predominant in the tall-grass vegetation, which averages 20 cm in vegetation height (Schütz and others 2006). Short-grass vegetation developed in areas where cattle and sheep rested (high nutrient input) during agricultural land-use (from 14th century until 1914); tall-grass vegetation developed in areas where cattle and sheep used to graze, but did not rest (Schütz and others 2003, 2006). Herbivores were shown to consume > 60% of the biomass in short-grass compared to < 20% in tall-grass vegetation (Schütz and others 2006). The herbivore community present in the SNP can be divided into four groups based on body size/weight: large [red deer (Cervus elaphus L.) and chamois (Rupricapra rupricapra L.); 30 - 150 kg], medium [marmot (Marmota marmota L.) and snow hare (Lepus timidus L.); 3 – 6 kg], and small vertebrate herbivores (small rodents: e.g. Clethrionomys spp., Microtus spp., Apodemus spp.; 30 – 100 g) as well as invertebrates (e.g. grasshoppers, caterpillars, cicadas, < 5 g).     Experimental design  We selected 18 subalpine grassland sites (9 short-grass, 9 tall-grass vegetation). The sites were spread across the entire park on dolomite parent material at altitudes of 1975 to 2300 meters. At each site we established an exclosure network (fences) in spring 2009 (early June), immediately after snowmelt. Each exclosure network consisted of a total of five 2 × 3 m sized plots that progressively excluded the different herbivores listed above (further labeled according to the herbivore guilds that had access to the respective plots “All”, “Marmot/Mice/Invertebrates”, “Mice/Invertebrates”, “Invertebrates”, “None”). The “All” treatment was thus accessible to all herbivores, was not fenced and was located at least 5 m away from a 2.1 m tall and 7 × 9 m main fence that enclosed the other four treatments. This fence was constructed of 10 × 10 cm wooden posts and electrical equestrian tape (AGRARO ECO, Landi, Bern, Switzerland; 20 mm width) mounted at 0.7 m, 0.95 m, 1.2 m, 1.5 m and 2.1 m above the ground that were connected to a solar charged battery (AGRARO Sunpower S250, Landi, Bern, Switzerland). We also mounted non-electrically charged equestrian tape at 0.5 m to help exclude deer and chamois, yet allow marmots and hares to enter safely. Within each main fenced area we randomly established four 2 × 3 m plots: (1) The “Marmot/Mice/Invertebrates” plot remained unfenced, thus, with the exception of red deer and chamois, all herbivores were able to access the plot, (2) The “Mice/Invertebrates” plot consisted of a 90 cm high electric sheep fence (AGRARO Weidezaunnetz ECO, Landi, Bern, Switzerland; mesh size 10 × 10 cm) connected to the solar panel and excluded all medium sized mammals (marmots, hares), but provided access for small mammals and invertebrates, (3) The “Invertebrates” plot provided access for invertebrates only and was surrounded by 1 m high metal mesh (Hortima AG, Hausen, Schweiz; mesh size 2 × 2 cm), (4) The “None” plot was surrounded by a 1 m tall mosquito net (Sala Ferramenta AG, Biasca, Switzerland; mesh size 1.5 × 2 mm) to exclude all herbivores. This plot was covered with a roof constructed of a wooden frame lined with mosquito mesh that was mounted on the wooden corner posts. We also treated this plot with a biocompatible insecticide (Clean kill original, Eco Belle GmbH, Waldshut-Tiengen, Germany) when needed to remove insects that might have entered during data collection or that hatched from the soil.     !!! The here published data set only contains data for “All”, and “Marmot/Mice/Invertebrates” (= ungulates excluded) plots !!!    Data collection  In-situ soil CO2 emissions were measured with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA) on two randomly selected locations on one subplot within each of the 90 plots. For each measurement the soil chamber (15 cm high; 10 cm diameter) was placed on a permanently installed PVC collar (10 cm diameter) driven five centimeters into the soil at the beginning of the study (June 2009). The measurements were conducted between 0900 and 1700 hours every two weeks from early to early September 2009, 2010, 2011 and 2013. Freshly germinated plants growing within the PVC collars were removed prior to each measurement to avoid measuring plant respiration/photosynthesis. The two measurements collected per plot every two weeks were averaged.     Please acknowledge the funding of the study: funded by the Swiss National Science Foundation, SNF grant-no 31003A_122009/1 to Anita C. Risch, Martin Schütz and Flurin Filli	proprietary
-soil-sealing-barcelona-milan_1.0	Soil sealing Barcelona and Milan different territorial levels	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	1.4941406, 41.1641817, 9.4523621, 45.6562879	https://cmr.earthdata.nasa.gov/search/concepts/C2789818009-ENVIDAT.umm_json	"__Dataset description__<br />   This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:      * a)	Barcelona city boundaries   * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)   * c)	Barcelona greater city (Urban Atlas)   * d)	Barcelona functional urban area (Urban Atlas)   * e)	Milan city boundaries   * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)   * g)	Milan greater city (Urban Atlas)   * h)	Milan functional urban area (Urban Atlas)<br />     In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). <br /> <br />       <br /> __Dataset composition__<br />   The dataset is provided in .csv format and is composed of: <br />     _IMD15_BCN_MI_Sources.csv_: Information on data sources <br />      _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:    * a)	Barcelona city boundaries (label: bcn_city)    * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)   * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)   * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)<br />      _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:    * e)	Milan city boundaries (label: mi_city)   * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)   * g)	Milan greater city (Urban Atlas) (label: mi_grc)   * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)<br />     _IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). <br />     Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. <br />     In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:     * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);   * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);   * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;    * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.<br /> <br />     <br /> __Further information on the Dataset__<br />   This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.<br />      1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.      2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.     3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.<br />     Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). <br />     Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:     * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option ""Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.    * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the ""overwrite"" option has been selected. <br />    <br /> Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.<br />     The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset."	proprietary
 soil125r_309_1	Data over the SSA in Raster Format and AEAC Projection	ORNL_CLOUD STAC Catalog	1980-01-01	1996-12-31	-106.43, 53.33, -103.83, 54.5	https://cmr.earthdata.nasa.gov/search/concepts/C2807648392-ORNL_CLOUD.umm_json	GIS layers that describe the soils of the BOREAS SSA.  Original data were submitted as vector layers that were then gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection.	proprietary
 soil_respiration_point_645_1	SAFARI 2000 Annual Soil Respiration Data (Raich and Schlesinger 1992)	ORNL_CLOUD STAC Catalog	1963-01-01	1992-01-01	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788352762-ORNL_CLOUD.umm_json	This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992 subset for the Safari 2000 project. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes.	proprietary
 soilt20r_357_1	BOREAS TE-20 Soils Data over the NSA-MSA and Tower Sites in Raster Format	ORNL_CLOUD STAC Catalog	1994-01-01	1994-12-31	-98.68, 55.87, -98.27, 55.94	https://cmr.earthdata.nasa.gov/search/concepts/C2927742817-ORNL_CLOUD.umm_json	Gridded from vector layers of soil maps received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994.The vector layers were gridded into raster files that cover the NSA-MSA and tower sites.	proprietary
 soilt20v_533_1	BOREAS TE-20 Soils Data over the NSA-MSA and Tower Sites in Vector Format	ORNL_CLOUD STAC Catalog	1994-01-01	1994-12-31	-98.79, 55.73, -98.09, 56.06	https://cmr.earthdata.nasa.gov/search/concepts/C2808093369-ORNL_CLOUD.umm_json	Vector layers of soil maps. The vector layers were converted to ARC/INFO EXPORT files.  These data cover 1-km diameters around each of the NSA tower sites and another layer covers the NSA-MSA.	proprietary
 soilte1r_312_1	BOREAS TE-01 Soils Data over the SSA Tower Sites in Raster Format	ORNL_CLOUD STAC Catalog	1994-01-01	1994-12-31	-106.21, 53.62, -104.61, 54	https://cmr.earthdata.nasa.gov/search/concepts/C2927721796-ORNL_CLOUD.umm_json	Gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01, who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the SSA tower sites.	proprietary
-solar-biomass-additional-references_1.0	Linking solar and biomass resources to generate renewable energy: can we find local complementarities in the agricultural setting?	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083029-ENVIDAT.umm_json	Additional references to the article:  Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting? Gillianne Bowman, Thierry Huber, Vanessa Burg Energies, https://www.mdpi.com/1996-1073/16/3/1486   Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems.	proprietary
 soller_wetlands_674_1	LBA Regional Freshwater Wetlands, 1-Degree (Stillwell-Soller et al.)	ORNL_CLOUD STAC Catalog	1995-01-01	1995-09-01	-85, -25, -30, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2777324266-ORNL_CLOUD.umm_json	This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format.The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.	proprietary
 sondecpexcv_1	Radiosondes CPEX-CV	GHRC_DAAC STAC Catalog	2022-09-01	2022-09-29	-23.400798, 0.053658, -0.073876, 16.789384	https://cmr.earthdata.nasa.gov/search/concepts/C2748663117-GHRC_DAAC.umm_json	The Radiosonde CPEX-CV dataset was collected during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign was based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. These data files are available from September 1, 2022, through September 29, 2022  in netCDF-4 format.	proprietary
 sonobuoy_whale_SO_Not provided	Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC	SCIOPS STAC Catalog	2001-03-21	2001-08-28	-77.2, -70.3, -61.5, -59	https://cmr.earthdata.nasa.gov/search/concepts/C1214155588-SCIOPS.umm_json	Mysticete whale calls were monitored/recorded via deployment of             directional sonobuoys during March-August 2001.  This monitoring             technique is used to study whale distribution, behavior and aid in             estimating populations.  Deployments were either random or when whales             were observed.  The observed calls are identified by species.             Ancillary calls by seals are reported but not identified by species.             The survey area included the continental margin to the west of the             Antarctic Peninsula extending from the northern tip of Adelaide Island             to the southern portion of Alexander Island, Crystal Sound, and             Marguerite Bay.          Ship names/cruise ID/cruise dates                R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001                RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001                RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001          Access to the original acoustic recordings should be directed to the             Investigator identified in this description.	proprietary
-source-code-climate-change-scenarios-at-hourly-resolution_1.0	Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789816944-ENVIDAT.umm_json	This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling    This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper.    This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.	proprietary
-sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0	Sources and turnover of soil organic matter in Pfynwald irrigation experiment	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083043-ENVIDAT.umm_json	This dataset contains all data on which the following publication below is based.    Paper Citation:  Guidi, C., Lehmann, M.M., Meusburger, K., Saurer, M., Vitali, V., Peter, M., Brunner, I., Hagedorn, F. (accepted). Tracing sources and turnover of soil organic matter in a long-term irrigated dry forest using a novel hydrogen isotope approach. Soil Biology and Biochemistry.    Please cite this paper together with the citation for the datafile.    Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris).  Data include: (1) Isotopic composition (stable isotope ratios of non-exchangeable hydrogen δ2Hn, carbon δ13C, and nitrogen δ15N) and Hn, C and N concentrations in SOM sources (fresh Pinus sylvestris needles, litter layer, fine roots), bulk SOM (organic layer, 0-2 cm, 2-5 cm, 60-80 cm), particle-size fractions (depths:  0-2 cm, 2-5 cm; cPOM: coarse POM; fPOM: fine POM; MOM: mineral-associated organic matter); (2) Mass loss, δ2Hn values and Hn concentrations of Pinus sylvestris fine roots and needle litter (litter decomposition experiments from Herzog et al. 2019, ISME journal, and Guidi et al. 2022, Global Change Biology); (3) Relative source contribution (foliar litter, fine roots, and mycelia) to bulk SOM and fractions estimated using Bayesian mixing models (R package MixSIAR, version 3.1.12) with irrigation and depth as fixed factors. The models were informed with δ13C, δ15N and δ2Hn values and C, N, and Hn concentrations of foliar litter, roots, and mycelia as input sources. Given the kinetic isotope fractionation occurring during microbial SOM decomposition, the mixing models were informed with isotope fractionation factors, representing the isotope enrichment from sources to soils; (4) Fraction of new organic Hn (Fnew) over the irrigation period, calculated using a simple end-member mixing model according to Balesdent et al. (1987) and mean residence time estimated as MRT = - t / ln (1 - Fnew), with t time in years since irrigation started and assuming single-pool model with first-order kinetics.	proprietary
 sowers_0739491_Not provided	2008 South Pole Firn Air Methane Isotopes	SCIOPS STAC Catalog	2008-12-01	2009-01-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json	This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica.  The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br).  The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity.  The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air.  The project will involve two phases:  1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories.  Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport.	proprietary
-spatial-modelling-of-ecological-indicator-values_1.0	Spatial modelling of ecological indicator values	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817163-ENVIDAT.umm_json	"Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.     This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection (""+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs""). The excel file (xlsx) provides a short description of the raster layers.    Paper Citation:    Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)"	proprietary
-spatial-planning-brazil_1.0	Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	-46.1425781, -24.005155, -44.4836426, -23.1908626	https://cmr.earthdata.nasa.gov/search/concepts/C2789817270-ENVIDAT.umm_json	"The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.    We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.    1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase    We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.     Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).    To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).     The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.    2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:    The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).    3)	Topographic drivers    To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication)."	proprietary
-species-distribution-maps-gdplants_1.0	Species distribution maps of Fagales and Pinales (GDPlants)	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789817446-ENVIDAT.umm_json	This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).   To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).	proprietary
 spectra_licor_47_1	Leaf Reflectances: LICOR (OTTER)	ORNL_CLOUD STAC Catalog	1990-06-19	1990-06-21	-122.61, 44.29, -121.33, 44.67	https://cmr.earthdata.nasa.gov/search/concepts/C2804756210-ORNL_CLOUD.umm_json	The variability of bi-directional spectral reflectance of cut conifer foliage between age classes, species and sites, measured by LICOR	proprietary
 spectra_perkin_48_1	Leaf Reflectances: Perkin-Elmer (OTTER)	ORNL_CLOUD STAC Catalog	1991-06-14	1991-06-18	-123.95, 44.6, -122.61, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804762963-ORNL_CLOUD.umm_json	Absolute (diffuse & specular) reflectance of leaves measured in the lab by Perkin-Elmer spectrophotometer to aid in understanding remotely sensed spectral data	proprietary
 spectra_se590_altitude_86_1	SE-590 Low Altitude Reflectances (OTTER)	ORNL_CLOUD STAC Catalog	1990-08-13	1991-07-03	-123.95, 44.29, -121.33, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804788870-ORNL_CLOUD.umm_json	Low altitude (Ultralight) spectral reflectances of OTTER research sites measured by Spectron SE590 spectrophotometer	proprietary
@@ -18740,9 +18054,6 @@ spectra_se590_field_80_1	SE-590 Field-Meas. Reflectances (OTTER)	ORNL_CLOUD STAC
 spectra_se590_lab_83_1	SE-590 Lab-Measured Reflectances (OTTER)	ORNL_CLOUD STAC Catalog	1990-08-17	1990-12-04	-123.95, 44.29, -121.33, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804776618-ORNL_CLOUD.umm_json	Laboratory hemispherical reflectance spectra measurements taken to eliminate the effects of atmosphere, understory, exposed soils, mixed species and canopy architecture	proprietary
 spectra_se590_landscape_84_1	SE-590 Landscape Reflectances (OTTER)	ORNL_CLOUD STAC Catalog	1990-10-06	1990-10-11	-123.95, 44.29, -121.33, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804776928-ORNL_CLOUD.umm_json	Bidirectional spectal reflectance factors of landscape elements (litter, soil, bark, scrubs & grasses, leaves) measured by Spectron SE590 spectroradiometer	proprietary
 spectra_target_74_1	Reflectance Reference Targets (OTTER)	ORNL_CLOUD STAC Catalog	1991-05-15	1991-08-24	-123.95, 44.29, -121.33, 45.07	https://cmr.earthdata.nasa.gov/search/concepts/C2804772156-ORNL_CLOUD.umm_json	Spectral reflectance measurements of flat field targets as reference points representative of pseudo-invariant targets as measured by Spectron SE590 spectrophotometer	proprietary
-spherical-model-snow-compression-3dct_1.0	Unconfined compression experiments and 3D CT images of spherical model snow and RG snow samples	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.8472691, 46.8125931, 9.8472691, 46.8125931	https://cmr.earthdata.nasa.gov/search/concepts/C2789817624-ENVIDAT.umm_json	For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles.   In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).	proprietary
-spot6-avalanche-outlines-16-january-2019_1.0	SPOT6 Avalanche outlines 16 January 2019	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	7.8744507, 46.5266506, 10.5152893, 47.3248149	https://cmr.earthdata.nasa.gov/search/concepts/C2789817742-ENVIDAT.umm_json	Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)    The generation of the data is described in:    Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.    The data was comprehensivly validated in a subset area in     Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021	proprietary
-spot6-avalanche-outlines-24-january-2018_1.0	SPOT6 Avalanche outlines 24 January 2018	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.6522217, 45.8900082, 10.4754639, 47.15984	https://cmr.earthdata.nasa.gov/search/concepts/C2789817830-ENVIDAT.umm_json	Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)    The generation of the data is described in:    Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.    Abstract.   Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).     We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.     This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.    Koordinate System: CH1903+ LV95 LN02	proprietary
 spot_3s_437_1	BOREAS Level-3S SPOT Imagery: Scaled At-Sensor Radiance in LGSOWG Format	ORNL_CLOUD STAC Catalog	1994-04-17	1996-08-30	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2929066675-ORNL_CLOUD.umm_json	For BOREAS, the level-3s SPOT data, along with the other remotely sensed images,were collected in order to provide spatially extensive information over the primary study areas.  This information includes radiant energy, detailed landcover, and biophysical parameter maps such as FPAR and LAI. The SPOT images acquired for the BOREAS project were selected primarily to fill temporal gaps in the Landsat TM image data collection.	proprietary
 spot_veg_burned_790_1	SAFARI 2000 Global Burned Area Map, 1-km, Southern Africa, 2000	ORNL_CLOUD STAC Catalog	2000-01-01	2000-12-31	-18, -35, 55, 18	https://cmr.earthdata.nasa.gov/search/concepts/C2789728878-ORNL_CLOUD.umm_json	The Global Burned Area 2000 initiative (GBA2000) was launched by the Global Vegetation Mapping Unit of the Joint Research Centre of the European Commission, in partnership with several other institutions, to develop reliable and quantitative information on the global magnitude and spatial distribution of biomass burning. The objective of GBA2000 was to produce a map of the areas burned globally for the year 2000, using the medium resolution satellite imagery provided by the SPOT-VEGETATION (VGT) system and to derive statistics of area burned per type of vegetation cover. A subset of the global GBA20000 map was prepared for SAFARI 2000 to map the area burned in sub-Saharan Africa during 2000 on a monthly basis using VGT imagery at 1 km spatial resolution. Burned areas were identified with a classification tree, relying only on the near-infrared channel of VGT. The data used in this work are in the S1 daily synthesis format, i.e. the data are radiometrically calibrated, precisely geo-located, and corrected for atmospheric effects.The data are binary image files of area burned, BSQ format in geographic projection.  There is one file for each month of 2000 and one file for all of the year 2000. There is also a comma-delimited ASCII text file that provides geographic coordinates (latitude and longitude) of the center of each pixel indicated as a burned area for all of 2000.	proprietary
 srb_clouds_1deg_1073_1	ISLSCP II Cloud and Meteorology Parameters	ORNL_CLOUD STAC Catalog	1986-01-01	1995-12-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2785316430-ORNL_CLOUD.umm_json	This data set contains cloud and meteorology data on a 1.0 degree x 1.0 degree spatial resolution. There are eight data files (*.zip) with this data set for several cloud parameters (monthly only) and meteorological parameters including monthly surface skin temperature, monthly total column ozone, and water vapor burdens for the period 1986-1995. All monthly parameters include files with a monthly mean value, a monthly standard deviation, and monthly minimum and maximum values. 	proprietary
@@ -18751,47 +18062,19 @@ srfmetmd_249_1	BOREAS Derived Surface Meteorological Data	ORNL_CLOUD STAC Catalo
 srtm_water_body_Not provided	SRTM Water Body Data	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567909-USGS_LTA.umm_json	  The SRTM Water Body Data files are a by-product of the data editing performed by the National Geospatial-Intelligence Agency (NGA) to produce the finished SRTM Digital Terrain Elevation Data Level 2 (DTED® 2). In accordance with the DTED® 2 specification, the terrain elevation data have been edited to portray water bodies that meet minimum capture criteria. Ocean, lake and river shorelines were identified and delineated. Lake elevations were set to a constant value. Ocean elevations were set to zero. Rivers were stepped down monotonically to maintain proper flow. After this processing was done, the shorelines from the one arc second (approx. 30-meter) DTED® 2 were saved as vectors in ESRI 3-D Shapefile format.  In most cases, two orthorectified image mosaics (one for ascending passes and one for descending passes) at a one arc second resolution were available for identifying water bodies and delineating shorelines in each 1 x1 cell. These were used as the primary source for water body editing. The guiding principle for this editing was that water must be depicted as it was in February 2000 at the time of the shuttle flight. A Landcover water layer and medium-scale maps and charts were used as supplemental data sources, generally as supporting evidence for water identified in the image mosaics. Since the Landcover water layer was derived mostly from Landsat 5 data collected a decade earlier than the Shuttle mission and the map sources had similar currency problems, there were significant seasonal and temporal differences between the depiction of water in the ancillary sources and the actual extent of water bodies in February 2000 in many instances. In rare cases, where the SRTM image mosaics were missing or unusable, Landcover was used to delineate the water in the SRTM cells. The DTED® header records for those cells are documented accordingly.  	proprietary
 ssafcovr_251_1	BOREAS Forest Cover Data Layers Over the SSA-MSA in Raster Format	ORNL_CLOUD STAC Catalog	1993-01-01	1993-12-31	-105.19, 53.75, -104.49, 54.1	https://cmr.earthdata.nasa.gov/search/concepts/C2807622777-ORNL_CLOUD.umm_json	Raster files created by processing original vector data.  Data include information of forest parameters for the BOREAS SSA MSA.	proprietary
 ssafcovv_509_1	BOREAS SERM Forest Cover Data Layers of the SSA in Vector Format	ORNL_CLOUD STAC Catalog	1993-01-01	1993-12-31	-110.05, 50.57, -94.08, 59.34	https://cmr.earthdata.nasa.gov/search/concepts/C2808092515-ORNL_CLOUD.umm_json	This data set was prepared by the SERM-FBIU.  The data include information on forest parameters and cover the area in and near the BOREAS SSA, excluding the PANP.	proprietary
-stability-tests-avalanche-observations-switzerland-norway_1.0	Stability tests, avalanche observations, Switzerland, Norway	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	4.0429688, 44.0286335, 33.2226563, 71.0474315	https://cmr.earthdata.nasa.gov/search/concepts/C2789817868-ENVIDAT.umm_json	Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.  The data set consists of     - Rutschblock test results (Switzerland)    - Extended Column Test results (Switzerland, Norway)    - Avalanche occurrence data (Switzerland, Norway).    The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).  For further information regarding the data, please refer to the publication or contact the author.	proprietary
-stable-water-isotopes-in-snow-and-vapor-on-the-weissfluhjoch_1.0	Stable Water Isotopes in snow and vapor on the Weissfluhjoch	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.8094821, 46.8298249, 9.8094821, 46.8298249	https://cmr.earthdata.nasa.gov/search/concepts/C2789817892-ENVIDAT.umm_json	"Notice:  Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.    This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).   Snow profiles and surface samples are available at different frequencies for each campaign. Please see ""Data_description.pdf"" for details.     Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided."	proprietary
-stand-inventory-data-from-the-10-ha-forest-research-plot-in-uholka-ukraine_1.0	Stand inventory data from the 10‐ha forest research plot in Uholka, Ukraine	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	23.6268711, 48.2747904, 23.6268711, 48.2747904	https://cmr.earthdata.nasa.gov/search/concepts/C2789817954-ENVIDAT.umm_json	In 2000, a permanent forest plot of 10 ha has been established in the core zone of the primeval beech forest of Uholka.   All living and dead trees with a diameter at breast height (DBH) ≥ 60 mm were identified to species, DBH measured, stems tagged and mapped.   Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories.   In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka‐Shyrokyi Luh forest. This data set allows us to derive a high‐resolution digital elevation model (DEM) of the plot area.  The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests.	proprietary
-stand_density_sdi-29_1.0	Stand density (SDI)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817931-ENVIDAT.umm_json	The Stand Density Index (SDI) is a general measure for the density of a stocking and is based on the number of stems/ha and the average diameter of the tally trees on the sample plot.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-stated-preference-data-on-the-insurance-value-of-forests-in-switzerland_1.0	Stated preference data on the insurance value of forests in Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817992-ENVIDAT.umm_json	We present stated preference data for improved forest management measures from seven Swiss municipalities in the Cantons of Grisons and Valais. The data was collected between October 2019 and February 2020 using an online questionnaire. We invited 10289 households to participate and received 939 responses. The online questionnaire consisted of two main parts: (i) an online choice experiment and (ii) questions on the sociodemographic characteristics of the responding households. The choice experiment confronted households with twelve consecutive choice tasks. Each choice task consisted of three options with a varying degree of avalanche and rock fall risk reduction due to improved forest management. The options further differed with respect to the way the costs for the improved forest management are allocated and the way they are calculated. We additionally provided each of the options with a cost attribute, allowing for the calculation of willingness to pay measures. At the end of the choice experiment we asked five de-briefing questions and eight attitudinal questions. Additionally, we asked the responding households to state their willingness to take risks. The sociodemographic characteristics collected in the second part of the questionnaire allow for an analysis of the impact they have on the choices we observed in the first part of the questionnaire.	proprietary
 stations_drainage_modelling_1	Drainage modelling for Australia's year-round Stations in Antarctica and at Macquarie Island	AU_AADC STAC Catalog	1994-01-04	1997-02-12	62.8375, -68.5836, 158.9461, -54.4931	https://cmr.earthdata.nasa.gov/search/concepts/C1214313834-AU_AADC.umm_json	This GIS dataset is the result of modelling of surface water drainage for Australia's year-round stations in Antarctica (Casey, Davis, Mawson) and at Macquarie Island. This was done by the Australian Antarctic Data Centre in 2000 at the request of Dr Martin Riddle and Dr Ian Snape of the Australian Antarctic Division.  The modelling was done using ESRI's ArcInfo workstation. A digital elevation model (DEM) was first created from the the Australian Antarctic Data Centre's topographic data, principally surface contours, and then drainage basins and drainage paths were derived from the DEM. The drainage is predicted surface flow due to changes in elevation and doesn't take account of any other processes. Several DEMs were created for each station at different spatial extents and resolutions.  The origin of the topographic data was mapping from aerial photography. The aerial photography was flown on 4 January 1994 (Casey), 11, 12 February 1997 (Davis), 7 December 1994 (Macquarie Island) and 18 March 1996 (Mawson).  The data available for download includes for each station:  1 the DEMs and the topographic data from which they were created; and 2 the predicted drainage basins and drainage paths. The data was originally created in ESRI's coverage (vector) and grid (raster) formats. It is provided here in ESRI's file geodatabase format. Documentation is included with the data.  The modelling was done as an aid to fuel spill contingency planning and the predicted drainage paths were used in the production of a spill risk assessment map for each station to go with the Australian Antarctic Division's fuel spill contingency plan for each station. The maps are available from the SCAR Map Catalogue (see a Related URL) and have catalogue numbers 13702 to 13705.  Validation of the modelling for Casey is described in M.J.Riddle, I.Snape, D.T.Smith and A.Z.Woinarski, 'Development and validation of a GIS-based dispersion model for oil spills in snow covered ground' in Proceedings of the 3rd International Conference Contaminants in Freezing Ground, Hobart 14-18 April 2002 Figures 1 and 2 in this paper are available from the SCAR Map Catalogue and have catalogue numbers 12930 and 12931.	proprietary
-stem-and-branchwood-data-swiss-nfi_1.0	Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083217-ENVIDAT.umm_json	In the Swiss National Forest Inventory (NFI) the volume of the stem and of large (≥ 7cm in diameter) and small branches is estimated based on allometric functions. These functions were developed based on data collected within the permanent plot network of the Experimental Forest Management (EFM) sites at WSL (David I. Forrester; Hubert Schmid; Jens Nitzsche (2021). The Experimental Forest Management network. EnviDat. doi: 10.16904/envidat.213). The data  were converted to digital format in two separate steps in the mid-1970s for stemwood and the mid-1980s for branchwood.  The dataset on stemwood volume contains 38’864 single tree data for the mean crosswise diameter at two meter sections along the stem plus an additional measurements at 1.3 m (i.e. DBH) where the diameter is greater than or equal to 7 cm (i.e. threshold of merchantable wood) and the lengths of the stem from the base to the threshold of merchantable wood and to the tree top. The measurements were collected on 768 EFM sites in the period 1888 to 1974. The dataset on branchwood is based on a subset of the stemwood data and contains in the raw format information on 14'712 single trees. It includes aggregated data from the stemwood dataset, i.e. the DBH, the stem-diameter at 7 m from the base, and the tree height from the base to the top, as well as the measured volume of large and small branches.  In 2022, the metadata of both datasets were checked, values were examined for plausibility and duplicated entries. Duplicates were removed as far as possible and the branchwood volume data were appended to the stemwood dataset to obtain a final, single file with matching single tree data. Following this evaluation the final dataset consisted of a total of 38’841 trees including 14’038 trees with measured branchwood data.	proprietary
-stem_count_of_young_forest-191_1.0	Stem count of young forest	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816843-ENVIDAT.umm_json	# 191#  Number of regeneration trees  starting at 10 cm tall up to 11.9 cm dbh recorded in NFI’s regeneration survey.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-stem_number-73_1.0	Stem number	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816985-ENVIDAT.umm_json	Number of stems of living trees and shrubs (standing and lying)  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-stem_number_of_dead_wood-116_1.0	Stem number of dead wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817146-ENVIDAT.umm_json	Number of stems of dead trees and shrubs (standing and lying)  starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-stem_number_of_dead_wood_nfi1-248_1.0	Stem number of dead wood NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817296-ENVIDAT.umm_json	Number of stems of dead trees and shrubs (standing and lying)  starting at 12 cm recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-stillberg-climate_1.0	Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.86716, 46.773573, 9.86716, 46.773573	https://cmr.earthdata.nasa.gov/search/concepts/C2789817441-ENVIDAT.umm_json	"# Important  This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry ""Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.."" The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg>  and with this DOI <https://doi.org/10.16904/envidat.396>."	proprietary
-stillberg-reforestation_1.0	Long-term treeline research dataset at Stillberg, Davos	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.86716, 46.773573, 9.86716, 46.773573	https://cmr.earthdata.nasa.gov/search/concepts/C2789817572-ENVIDAT.umm_json	"# Important  This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry ""Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland."" The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg>  and with this DOI 10.16904/envidat.397."	proprietary
 stillwell_geology_gis_1	Geology of the Stillwell Hills GIS Dataset	AU_AADC STAC Catalog	1996-09-30	2016-07-28	59.3618, -67.4645, 59.5698, -67.2942	https://cmr.earthdata.nasa.gov/search/concepts/C1338626905-AU_AADC.umm_json	The Stillwell Hills region comprises granulite-facies gneisses which record evidence for multiple episodes of deformation and metamorphism spanning more than 2500 million years. The predominant orthogneiss package (Stillwell Orthogneiss) is thought to represent the margin of an Archaean craton exposed in Enderby Land, some 150 km to the west that was reworked during the late Proterozoic. Younger additions to the crust include Palaeoproterozoic charnockitic gneiss (Scoresby Charnockite) and Meso-Neoproterozoic mafic sills and dykes (Point Noble Gneiss, Kemp Dykes) and felsic pegmatites (Cosgrove Pegmatites). Subordinate supracrustal rocks, including metaquartzite, metapelitic, metapsammitic and calc-silicate gneiss (Dovers Paragneiss, Sperring Paragneiss, Stefansson Paragneiss, Keel Paragneiss, Ives Paragneiss) are intercalated and infolded with the Archaean-Palaeoproterozoic orthogneisses.  This Dataset is derived from the map product 'The Geology of the Stillwell Hills, Antarctica'.  This metadata record was created using information in Geoscience Australia's metadata record at  http://www.ga.gov.au/metadata-gateway/metadata/record/78535/	proprietary
-streamwater-level-and-isotopic-composition-four-rainfall-events-studibach_1.0	Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.7178659, 47.0380179, 8.7178659, 47.0380179	https://cmr.earthdata.nasa.gov/search/concepts/C2789817707-ENVIDAT.umm_json	"This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:     - Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);   - Rainfall at 5-minute resolution (mm per hour);   - Rainfall deuterium composition (‰);   - Stormflow deuterium composition (‰);   - Groundwater and baseflow deuterium compositions (‰).     For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions (""Deuterium_Rainfall"" and ""Deuterium_Streamwater""), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values (""Deuterium_Snapshot"") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).     More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019."	proprietary
-stumps-as-a-dead-wood-resource_1.0	Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817823-ENVIDAT.umm_json	Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.  The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.    The study is published in Annals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).  The data can be obtained from the authors upon reasonable request.	proprietary
 sua_pan_lai_fpar_778_1	SAFARI 2000 LAI and FPAR Measurements at Sua Pan, Botswana, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-20	2000-08-28	26.03, -20.53, 26.04, -20.52	https://cmr.earthdata.nasa.gov/search/concepts/C2789103004-ORNL_CLOUD.umm_json	The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to the Sua Pan salt playa in the Magkadigkadi region of Botswana during the SAFARI 2000 Dry Season Aircraft Campaign to collect various data sets for validating the MISR LAI/FPAR algorithm. Ground measurements of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) were made using the LAI-2000 plant canopy analyzer and Sunfleck PAR ceptometer, respectively, during focused periods from August 20 to August 28, 2000 at a dry grassland site adjacent to the Sua Pan. The 1 km by 1 km sampling grid was a homogeneous, relatively dense grassland, with a height of 20-100 cm and two prevalent grass types, Odyssea paucinervis and Sporobolus spicatus. Associated reflectance measurements were made with the PARABOLA and ASD instruments (Helmlinger et al., 2004a; 2004b).The data files contain measurements of LAI and PAR reflectance and transmission and a description of sky conditions during the sampling periods. With one exception, all measurements were made under clear sky conditions.  PAR data were measured only on the transect scale while LAI are provided at both pixel and transect scales. PAR readings were performed at 93 transect sample points, and LAI readings were performed at 135 (93 transect and 42 subgrid) sample points. Each file also contains mean LAI and PAR values. The data files are ASCII tables, in comma-separated-value format.	proprietary
 sua_pan_skukuza_brdf_779_1	SAFARI 2000 BRDF Measurements at Sua Pan and Skukuza, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-25	2000-10-02	26.03, -25.02, 31.48, -20.52	https://cmr.earthdata.nasa.gov/search/concepts/C2789638795-ORNL_CLOUD.umm_json	The Jet Propulsion Laboratory's (JPL) Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), version III, instrument collected radiance data covering both the upwelling and downwelling hemispheres at the Sua Pan salt playa in the Magkadigkadi region of Botswana and at the Skukuza tower site in the Kruger National Park, South Africa between August 25 and October 2, 2000 during in the SAFARI 2000 Dry Season Aircraft Campaign. PARABOLA III is a sphere-scanning radiometer that provides multi-angle measurements of sky and ground radiances on a spherical grid of 5 degrees in the zenith-to-nadir and azimuthal planes in eight spectral channels (444, 551, 581, 650, 860, 944, 1028, and 1650 nm). The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions for a vicarious calibration of the Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard the Terra satellite. Four of the PARABOLA channels (444, 551, 650, and 860 nm) are similar to those of the MISR sensor. Measurements were made on cloud-free days of Terra satellite overpasses.Each data file contains radiance counts (ASCII integer values) for 8 bands for each 3-minute data collection. Zenith and azimuth angles are implied by radiance count positions in file. Additional files contain average dark current readings, empirically determined by covering the detectors. The data can be processed to radiance using a series of second order polynomial fit coefficients, provided in the documentation file, and dark current offset. Site-specific auxiliary information is also provided, for each date of PARABOLA data collection.	proprietary
 sua_pan_surface_spectra_780_1	SAFARI 2000 Surface Spectral Reflectance at Sua Pan, Botswana, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-24	2000-09-03	26.03, -20.58, 26.08, -20.52	https://cmr.earthdata.nasa.gov/search/concepts/C2789642681-ORNL_CLOUD.umm_json	The Multi-angle Imaging SpectroRadiometer (MISR) Validation team was deployed to Sua Pan, a salt playa in the Magkadigkadi region of Botswana, from August 18 to September 4, 2000, during the SAFARI 2000 Dry Season Aircraft Campaign. The experiment was designed to collect data necessary for multi-angle top-of-atmosphere radiance predictions in order to provide a vicarious calibration of the MISR instrument aboard the Terra satellite. Reported here are ground-based reflectance measurements collected using an Analytical Spectral Devices (ASD) spectroradiometer at Sua Pan and adjacent grassland targets. The grasslands provided large homogeneous areas for comparison of scale between ground measurements and remote sensing results.Data files contain numeric values that represent mean reflectance over space (grassland) or wavelength range (pan), stored as ASCII files, one file per site, in comma-separated-value (.csv) format, with column headers. The Sua Pan data, collected over a 1 km x 2 km area, are presented as rows of mean reflectance (every 10 nm) for 20 points, where the mean represents the average local reflectance spectra collected within 150 m of the given latitude and longitude. The grassland data cover a 1 km2 area and are provided every nm from 1 to 2500. Each row of data contains a mean and standard deviation at a given wavelength, where the mean represents the average of 570 measurements taken over the 1 km2 area.Related data sets from Sua Pan provide ground measurements of BRDF, and LAI and FPAR (Helmlinger et al., 2004 and Buermann and Helmlinger, 2004, respectively).	proprietary
 summer_sea_salt_1000-2009_1	Annualized Summer Sea Salt From the Law Dome Ice Core Chemistry Record, 1000-2009	AU_AADC STAC Catalog	1987-01-01	2010-12-31	112.8, -66.77, 112.8, -66.77	https://cmr.earthdata.nasa.gov/search/concepts/C1214311373-AU_AADC.umm_json	This data set is the logged, annualised summer sea salt (December to March, DJFM) concentrations from the Law Dome ice core chemistry record, spanning 1000-2009 AD (dates apply to the year of JFM, so e.g. 1980 is an average of Dec 1979 and Jan-Mar 1980). The data are compiled from numerous ice cores drilled at the Law Dome site sequentially since 1987, and chronologically dated using volcanic horizons and annual layer counting.  The cores used are (chronologically from oldest data to newest):  DSS Main DSS97 DSS0102 DSS0809 DSS0910  The dataset has 37 'missing' summer values in instances where insufficient ice core material was available. These missing summers have been filled using linear interpolation.  This work forms part of Australian Antarctic Science (AAS) project no. 757.  The record was published as an ENSO and eastern Australian rainfall proxy record in:  Vance, T. R., T. D. van Ommen, M. A. J. Curran, C. T. Plummer, A. D. Moy, (2012): A millennial proxy record of ENSO and eastern Australian rainfall from the Law Dome ice core, East Antarctica. Journal of Climate, doi: 10.1175/JCLI-D-12-00003.1	proprietary
 sunphair_298_1	BOREAS RSS-12 Airborne Tracking Sunphotometer Measurements (C-130)	ORNL_CLOUD STAC Catalog	1994-05-25	1994-09-09	-106.75, 52.94, -97.38, 56.33	https://cmr.earthdata.nasa.gov/search/concepts/C2813389538-ORNL_CLOUD.umm_json	Contains measurements from the airborne auto tracking sun photometers on board the NASA Ames C-130 aircraft, operated by RSS12 (Wrigley).	proprietary
-survey-energy-transition-municipal-level-switzerland_1.0	Implementing the energy transition at municipal level in Switzerland: A regional survey	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	7.5270081, 46.8734277, 7.7714539, 47.0383456	https://cmr.earthdata.nasa.gov/search/concepts/C3226083086-ENVIDAT.umm_json	The dataset contains data from a survey, which was conducted in a periurban region close to Berne, Switzerland. The survey was conducted in Fall 2018 and contained opinion questions about the energy transition. Additionally, spatial data was collected using a PPGIS. While the opinion data is included in the data set, the spatial data is not. For more explanation, please consider the information sheet, the related publications or to contact the authors.	proprietary
-survey-of-spruce-seed-and-cone-insects-in-switzerland_1.0	Survey of spruce seed and cone insects in Switzerland	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817908-ENVIDAT.umm_json	In 1989 a nation-wide survey on spruce seed and cone insects was carried out at 29 locations distributed across the 5 main geographic regions of Switzerland. The cones were incubated in a controlled environment chamber and the emerging insects were collected and identified. The cones were kept for three years to allow diapausing insects to emerge. The methods are described in more detail in the corresponding publications.	proprietary
 survey_1997_V3_1	Macquarie Island Mapping Program Survey Field Work and Report Voyage 3 Round Trip November 1997	AU_AADC STAC Catalog	1997-11-11	1997-11-15	158.8, -54.8, 158.9, -54.4	https://cmr.earthdata.nasa.gov/search/concepts/C1214313903-AU_AADC.umm_json	"Taken from sections of the report:  1.	Introduction  This report details the survey work carried out on Macquarie Island during November 1997 by LANDINFO staff on behalf of the Australian Antarctic Division's Mapping Program. The main task of the survey team was to acquire aerial photography of the island to enable the production of a new topographic map of the island. Other tasks involved field checking the digital station area map (DSAM) and providing support to the tide gauge maintenance team.  The following team carried out the survey-mapping work: Tom Gordon		LANDINFO Surveyor Roger Handsworth	Antarctic Division Engineer  Although this report touches on the work carried out by Roger Handsworth and Rupert Summerson, it does not cover the specifics of their work.   2.	Project Brief  The survey-mapping brief lists the following tasks:  1.	Aerial Photography of the Island and station area. 2.	Aerial Photography of Judge and Clerk Island to the south and Bishop and Clerk Island to the north of Macquarie Island. 3.	Second order levelling from the tide gauge bench marks to AUS 211 4.	Updating the Digital Station Area map.  These tasks are listed in order of priority. A copy of the survey brief for Macquarie Island is included in Appendix A."	proprietary
 survey_2001_grove_1	Extension of the Australian Antarctic Geodetic Network in Grove Mountains - Survey Report 2001	AU_AADC STAC Catalog	2000-12-29	2001-01-11	75, -72.77, 75.1, -72.73	https://cmr.earthdata.nasa.gov/search/concepts/C1214311403-AU_AADC.umm_json	Taken from sections of the report:  ANARE planned geodetic survey work in the region in the summer 2000/2001. Initially it was planned to fly into the region from Davis wintering station on four separate days, allowing enough time to complete all the intended work. However owing to the harsh flying conditions and the nature of the terrain there was only time for two visits during the 2000 / 2001 summer season.  Although only two trips were made into the Grove Mountains a number of outcomes were achieved they included: -Establishment of a new geodynamic survey monument in the vicinity of Mount Harding, to strengthen the Antarctic geodetic network, and also assist with long term monitoring of crustal motion in Antarctica. -Several days of GPS data collected on the existing geodetic network point at Austin Nunatak - 60km to the West of Mount Harding -Search for two existing CHINARE geodetic control points established near Mount Harding and Zakharoff Ridge.  The work was carried out by Gary Johnston, Paul Digney and John Manning working for the Australian Surveying and Land Information Group (AUSLIG) - now Geoscience Australia (GA).	proprietary
 survey_2002-03_V2_V5_1	Mapping field program survey report summer 2002/2003	AU_AADC STAC Catalog	2002-12-06	2003-02-10	75, -70, 80, -67	https://cmr.earthdata.nasa.gov/search/concepts/C1214311404-AU_AADC.umm_json	Taken from sections of the Report:  The 2002-03 Mapping and Geographic Information Program (MAGIP) field season was undertaken from Davis Station. Nigel Peters from Sinclair Knight Merz undertook this season's fieldwork, the results of which are described in the following report.        The main objective for this season was to provide photo control mapping in the Rauer Group, with photo control also required at Davis Station and Marine Plains. A number of other tasks were undertaken in support of various scientific and engineering programs.  The tasks outlined in the surveyors brief are varied and numerous and have been included to provide the surveyor with a full and appropriate work program. The tasks are prioritised, usually with one or two major tasks with a number of minor tasks listed to be undertaken if the opportunity arises. This season's Survey Brief has been included in Appendix A with a summary of achievements listed in Appendix B.        The following report covers the fieldwork undertaken by myself during the 2002/2003 ANARE Summer Field Season. Data collected in support of other scientific programs has been included in this report primarily as a record of work undertaken by the mapping program. These data have been supplied to the various scientists for inclusion in their studies.  Sequence of Events  # 4th  November - 12th November 2002 -  Pre-Departure Training  - Field training for expeditioners at Bronte Park prior to the departure of V2.  - Survey briefing at Antarctic Division by Mapping Officer, Mr Henk Brolsma  # 20th November -5th December 2002 - Voyage 2  - Final preparation and checking and replacement of damaged equipment  - The Aurora Australis departed Hobart on the evening of 22nd November en route for Zhongshan, Davis and Mawson  - The Aurora Australis arrived of Zhongshan on the 3rd December where Chinese personal were deployed  - The Aurora Australis stopped approximately 1km off shore from Davis on the evening of the 4th December and arrived at Davis Station 5th December  # 6th December - 10th December  2002 - Davis Station  - Davis Resupply involving unloading and storage of food and equipment  # 11th December - 31st December 2002 - Davis Station   - Down loading Tide Gauge at Davis Station  - GPS measurements AUS303  - Coordination and levelling building Heights  - Coordination of control points Rauer Group  - Coordination of control points Davis  # 1st January - 20th  January 2003  - Davis Station  - Coordination of control points Rauer Group  - Antenna Farm levelling  - Surveys at Brooks, Bandits and Watts huts  # 21st January - 26th   January 2003 - Law Base  - Law Base Tide gauge downloading   - GPS connections to Davis   # 27th January - 9th February 2003 - Davis Station  - Tarbuck Crag repeater survey  - Skyline Survey Antenna Farm  - Establish new Tide Pole at Deep Lake  - Station duties loading equipment on to Ice Bird  # 10th February - 22nd February 2003 - Voyage 5  - Depart Davis   - Arrive Hobart 22nd February   Scope of Work        The Antarctic Mapping Officer Mr Henk Brolsma provided the scope of works within the Surveyors Brief for the 2002- 2003 field survey program (Appendix A). The following is a summation of the survey requirements for this season.  # Rauer Group   - Photo control are required throughout the Rauer Group at specified locations  # Davis   - Down Load Tide gauge  - Timed water level measurements  - Levelling between tide gauge benchmarks, including GPS observation  - Update station map and determine levels for all building floors, roof levels and the ground at the corner of every building  - Photo control for orthophoto at Davis and  at Heideman Bay  # Zhongshan  - Download tide gauge  - Timed water level measurements  - Height connection from Law Base to tide gauge bench mark  - Level between tide gauge benchmarks  - Check existing marks established for tide gauge location  # Vestfold Hills  - Deep Lake depth pole  - Take pole readings  - Repair depth pole  - Lake levelling  - Location of bench marks	proprietary
 survey_2004-2005_geodesy_1	Antarctic Geodesy Survey Report 2004-2005 summer - Samim Naebkhil and Michael Moore	AU_AADC STAC Catalog	2004-10-05	2005-03-31	60, -70, 112, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214311385-AU_AADC.umm_json	Taken from sections of the report:  Project officers Samim Naebkhil and Michael Moore from Geodetic operations within Geoscience Australia Earth Monitoring Group took part in the 2004/2005 Summer Antarctic Geodesy Program.  Samim Naebkhil left Hobart aboard Aurora Australis on Voyage 1 on the 5th of October, 2004 to Casey.  Michael Moore left aboard V2 in November 2004.  The 2004/2005 season had a number of aims and objectives to be meet. These included: - Reference Mark Survey of Australian Regional GPS network in Antarctica Casey AUS100, Davis AUS099 and Mawson AUS064. - Levelling from the GPS marks to the Tide Gauge Bench Marks - Upgrade of Australian Regional GPS network in Antarctica Casey AUS100, Davis AUS099, and Mawson AUS064. - Reference Mark Survey and Upgrade of Continuous GPS Sites (CGPS) AUS351 Grove Mountains, Wilson Bluff and Mt. Creswell. - Perform an ICESAT calibration at Mt. Creswell - GPS observation at Larseman Hills over Geodynamic mark AUS334 and Tide gauge Bench Mark NMVS278. - GPS observation over the Tide Gauge Bench Marks at Casey, Davis and Mawson. - Gravity observations at Casey, Davis and Mawson. - GPS observations over including the Chinese and Russian survey Marks to strength the Australian Geodetic Network and an establishment of a unified datum. - GPS observations over various Bench Marks in the Vestfolds hills to strengthen the geometric geoid. - Assist other science programs with Geodesy related support. Despite all efforts made to accomplish all the aimed tasks for this season, a number of these tasks could not be done and were beyond our control due to late arrival and associated problems with the CASA planes. The following tasks were not able to be done this season: - Reference Mark Survey of Mawson ARGN AUS064. - Orthometric Leveling from Mawson ARGN AUS064 to Tide Gauge Bench Marks - Reference Mark Survey and upgrades of CGPS sites Wilson Bluff and Mt. Creswell - Perform an ICESAT calibration at Mt. Creswell - Gravity connection Mawson.  This work contributed towards AAS (ASAC) project 1159.	proprietary
 survey_2006-2007_geodesy_1	Antarctic Geodesy Field Report 2006-2007 - N Brown and A Woods	AU_AADC STAC Catalog	2006-12-23	2007-02-25	60, -70, 101, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214311387-AU_AADC.umm_json	Taken from sections of the report:  In recent years, Geoscience Australia (GA) has increased its capability on the Antarctic continent with the installation of Continuous Global Positioning System (CGPS) sites in the Prince Charles Mountains and Grove Mountains. Over the course of the 2006/07 Antarctic summer, Alex Woods and Nick Brown from Geoscience Australia (GA) collaborated with Dan Zwartz of the Australian National University (ANU) to install new CGPS sites at the Bunger Hills and Richardson Lake and perform maintenance of the CGPS sites at the Grove Mountains, Wilson Bluff, Daltons Corner and Beaver Lake.  The primary aim of the CGPS sites is to provide a reference frame for Antarctica, which is used to determine the long-term movement of the Antarctic plate. Data from Casey, Mawson and Davis is supplied to the International GPS Service (IGS) and in turn used in the derivation of the International Terrestrial Reference Frame (ITRF). The sites also open up opportunities for research into post-glacial rebound and plate tectonics.  In many respects CGPS sites in Antarctica are still in their infancy. Since the mid 1990's Geoscience Australia and the Australian National University have been testing new technology and various methods to determine the most effective way of running a CGPS site in Antarctica.  A more detailed review of Australia's involvement in Antarctic GPS work can be found in (Corvino, 2004)  In addition, a reconnaissance survey was undertaken at Syowa Station to determine whether a local tie survey could be performed on the Syowa VLBI antenna in the future. Upgrades were made to the Davis and Mawson CGPS stations and geodetic survey tasks such as reference mark surveys, tide gauge benchmark levelling and GPS surveys were performed at both Davis and Mawson stations. In addition, work requested by Geoscience Australia's Nuclear Monitoring Project, the Australian Government Antarctic Division (AGAD) and the University of Tasmania (UTAS) were completed.  The 2006/07 Geoscience Australia Antarctic expedition proved to be one of the most successful Antarctic seasons by geodetic surveyors from Geoscience Australia. All intended field locations were visited and all work tasks were completed.  Background The primary aim of the CGPS sites is to provide a reference frame for Antarctica, which is used to determine the long-term movement of the Antarctic plate. Data from Casey, Mawson and Davis is supplied to the International GPS Service (IGS) and in turn used in the derivation of the International Terrestrial Reference Frame (ITRF). The sites also open up opportunities for research into post-glacial rebound and plate tectonics.  In many respects CGPS sites in Antarctica are still in their infancy. Since the mid 1990's Geoscience Australia and the Australian National University have been testing new technology and various methods to determine the most effective way of running a CGPS site in Antarctica.  Dr John Gibson from The University of Tasmania requested that Alex Woods and Nick Brown collect moss samples from any locations visited during the Antarctic summer field season. While working in the field only a few moss specimens were found. No moss or lichen specimens were observed at locations such as Wilson Bluff, Dalton Corner, Beaver Lake or the Grove Mountains. Moss samples were collected at Richardson Lake and Mawson Station and these samples were frozen after collection and returned to Australia.  This work contributed towards AAS (ASAC) project 1159.	proprietary
 survey_v6_2003_1	Macquarie Island survey, Voyage 6 March 2003	AU_AADC STAC Catalog	2003-03-24	2003-03-28	158.84, -54.63, 158.95, -54.46	https://cmr.earthdata.nasa.gov/search/concepts/C1214311407-AU_AADC.umm_json	"Voyage 6 of the Australian Antarctic Program for 2002/03 resupplied the station at Macquarie Island. Five days were spent at the island:  March 24 - 28. During this time some surveys were carried out by Paul Boland  (DPIWE, Tasmania) and David Smith (Australian Antarctic Data Centre).     Tasks carried out by Paul included the following:    (i) Detailed surveys of the huts and other infrastructure (eg generator  platforms, walkways) at Green Gorge and Bauer Bay, a nearby stream at Green  Gorge, nearby walking tracks at Bauer Bay. This work was done for Henk Brolsma  (AAD Mapping Officer).  Such features can potentially be used to rectify aerial photographs or satellite images. The Bauer Bay data have been used to rectify a Digital Globe satellite image of north-western Macquarie Island. Refer to the metadata  record with ID macquarie_quickbird_mosimage. A survey mark was also  established near the Bauer Bay hut.    (ii) A vegetation survey at Handspike Corner for Dr Dana Bergstrom (AAD RISCC program).   (iii) A survey of the old tip site, the Power House and the bunds at the  station for Dr Ian Snape and Dr John Rayner (AAD Human Impacts Program).    The data resulting from the surveys are available for downloading from three related URLs below.  1. The dxf file, spreadsheet with levelling data and annotated photos provided by Paul; 2. Shapefiles created from the point data in the dxf file and stored in the horizontal datum ITRF2000@2000 used by Paul (note: the dxf file needs to be referred to for descriptions of the points); 3. Shapefiles representing the features surveyed at the station for the Human Impacts Program, created from the point data in the dxf file and stored in the horizontal datum WGS84.   The transformation from ITRF2000@2000 to WGS84 for this data was carried out by applying ""The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N."" given on page 3 of the survey report ""Macquarie Island OSG  Survey Campaign, Voyage 8 Round Trip, March 2002"" by John VanderNiet and Nick Bowden.  For more information about this survey work please contact Henk Brolsma (AAD Mapping Officer).     A GPS base station was also set up for much of the resupply period with the antenna  mounted on the roof of the Biology building. Paul surveyed the antenna  position. Trimble .ssf, RINEX and .dat files were collected. This base station data and data collected by the Geoscience Australia permanent base station, MAC1, during the resupply period are available for download from a related URL below.       David used a Trimble Geoexplorer GPS to survey points at 5 metre intervals along two 50 metre transects laid out by Lee Belbin (Australian Antarctic Data Centre) near the Biology building at the station. At each point Pat Lewis (PhD  student, IASOS, University of Tasmania) collected invertebrates using a pooter for a fixed period of time while Perpetua Turner (AAD RISCC program) made notes about the vegetation and environment.  This work was done for Dr Penny Greenslade (ANU) and the samples and data were  given to her back at the AAD. The transect sample points were differentially corrected using the base station data and are available for download from a link below.  David also collected the locations of the two navigation guides on The Isthmus and Tractor Rock which is the southern extent of Station Limits on the east coast. These locations were also differentially corrected. The locations of the two navigation guides are available for download from the link below. The location of Tractor Rock is in the unofficial Australian Antarctic Gazetteer (see link below) as this name has not been approved by the Nomenclature Board of Tasmania."	proprietary
-swe-measurements-gnss-along-a-steep-elevation-gradient_1.0	Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.781732, 46.8296864, 9.8881513, 46.9133722	https://cmr.earthdata.nasa.gov/search/concepts/C2789817963-ENVIDAT.umm_json	This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS)  and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).	proprietary
-swe2hs-calibration-and-validation-data_1.0	SWE2HS model calibration and validation data	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.3723145, 44.9790475, 13.4802246, 47.9298398	https://cmr.earthdata.nasa.gov/search/concepts/C3226083104-ENVIDAT.umm_json	The data in this repository was used for the calibration and validation of the  SWE2HS model in the following publication:  Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258  Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.	proprietary
-swiss-biomass-potentials_1.0	Potentials of domestic biomass resources for the energy transition in Switzerland	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818004-ENVIDAT.umm_json	Switzerland has a reliable and cost efficient energy system. Due to phase  out of nuclear energy it is necessary to find new options to maintain this powerful energy system. The Swiss energy strategy 2050 aims to reduce CO2-emissions, increase efficiency and promote renewable energies. The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) examined relevant woody and non-woody biomass quantities (cubic meters, fresh-, dry weight) and their energy potentials (in Petajoules: primary energy and biomethane) with a similar methodological approach. The work was done within the frame of the Swiss Competence Centers for Energy Research (SCCER) especially in line with the SCCER Biomass for Swiss energy future (Biosweet). With a uniform and consistent approach for the current potentials ten biomass categories were estimated and aggregated for the whole of Switzerland. In this context solutions for the technical, social and political challenges are promoted. First, considering the different biomass resources characteristics and available data, appropriate methods at the finest scale possible were elaborated to estimate the annual quantities which could theoretically be collected (theoretical potential). Then, explicit and rational restrictions for sustainable bio-energy production were defined according to the current state of the art and subtracted from the theoretical potential to obtain the sustainable potential. The main restrictions are competing material utilizations, environmental factors and supply costs. Finally, the additional sustainable potential was estimated considering the current bioenergy production. Our main purpose was to provide potentials for developing conversion technologies as well as a detailed and comprehensive basis of the Swiss biomass potentials for energy use for economic and political decision makers. The complete report is available under https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A13277/datastream/PDF/view	proprietary
-swiss-canopy-crane-ii-research-site_1.0	Swiss Canopy Crane II research site	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.7762604, 47.4387988, 7.7762604, 47.4387988	https://cmr.earthdata.nasa.gov/search/concepts/C2789818064-ENVIDAT.umm_json	"![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg ""Swiss Canopy Crane II"")    This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species."	proprietary
-swiss-fluxnet-davos_1.0	Swiss FluxNet Site Davos	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.8559167, 46.8153333, 9.8559167, 46.8153333	https://cmr.earthdata.nasa.gov/search/concepts/C3226083201-ENVIDAT.umm_json	The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN).   Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method.   In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.    __Measurements__    - Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.  - Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).  - Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.  - Auxiliary micrometeorology and soil climate measurements.    __Data availability__    Near real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.    __Data policy__    ICOS data license:   [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)    __Detailed site info__:   [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)	proprietary
-swiss-fluxnet-lageren_1.0	Swiss FluxNet Site Lägeren	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.364389, 47.478333, 8.364389, 47.478333	https://cmr.earthdata.nasa.gov/search/concepts/C3226083256-ENVIDAT.umm_json	The Swiss FluxNet Site Lägeren is a managed mixed deciduous mountain forest located on the steep Lägeren mountain (NW of Zurich, Swiss Plateau). The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and the National Air Pollution Monitoring Network (NABEL). In addition to Swiss FluxNet, the site is part of the Long-term Forest Ecosystem Research (LWF) of WSL and the biological drought and growth indicator network (TreeNet) of WSL.        __Measurements__    - Ecosystem flux measurements of CO2, H2O vapour are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.  - Sub-canopy eddy fluxes (CO2, H2O), soil respiration campaigns  - Continuous CO2 profile measurements.  - Auxiliary micrometeorology and soil climate measurements.    __Data availability__    All data are available from the European Fluxes Database Cluster, but are also part of Fluxnet2015 dataset.    __Data policy__    ICOS data license:   [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)    __Detailed site info__:   [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae//](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)	proprietary
-swiss-municipalities-survey-on-spatial-planning-instruments_1.0	Swiss Municipal Spatial Planning Survey	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817630-ENVIDAT.umm_json	Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.	proprietary
-swiss_landscape_services_change_1.0	Land use projections and services for Switzerland	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817302-ENVIDAT.umm_json	Data and scripts of publication:    Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.    Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.     Data Folder:  Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored    Scripts Folder:  Contains scripts organized into subfolders depending on their purpose    Note: Some abbreviations within the scripts and data are derived from German words and not English.	proprietary
-swiss_lulc_forecast_21th_century_1.0	High resolution land use forecasts for Switzerland in the 21st century	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083049-ENVIDAT.umm_json	We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using >200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC  maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.	proprietary
-swissfungi-distribution-of-fungi-in-switzerland_1.0	SwissFungi - Records Database for the Fungi of Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817130-ENVIDAT.umm_json	This dataset provides distribution data of fungi in Switzerland of the National Data and Information Centre, called  [SwissFungi](https://swissfungi.wsl.ch/en/index.html). SwissFungi is a partner of [InfoSpecies](https://www.infospecies.ch/de/), the network of Swiss data and information centres for [fauna](http://www.cscf.ch/cscf/de/home.html), [flora](https://www.infoflora.ch/en/) and [fungi](https://swissfungi.wsl.ch/en/index.html). One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.    The SwissFungi database currently contains more than 670'000 geo-referenced fungi observations, distributed throughout Switzerland. The oldest observations date back to 1770. A large portion of the records are from the last two decades of the last century to the present day. The database is continuously updated with new fungi records. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.    The data from the distribution atlas of fungi in Switzerland are available for research and practice (nature conservation projects, environmental impact assessments etc.) and can be obtained via an [application form](https://www.infospecies.ch/de/assets/content/documents/Formular_Datenanfrage20190625.pdf). Please note the tariffs for data requests and submit your request directly to the [InfoSpecies](https://www.infospecies.ch/de/) office. Applications are usually answered within two working weeks. Details on the use of data are regulated in the current guidelines of the national data centers. Please note that the data center SwissFungi is not able to verify all incoming fungal records completely for a correct identification or coordinate errors and therefore cannot guarantee the correctness of the information.    License under [InfoSpecies](https://www.infospecies.ch/de/). Data is free of charge for research projects and available on request.	proprietary
-swisslichens_1.0	SwissLichens - Distribution of lichens in Switzerland	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817454-ENVIDAT.umm_json	This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.    The SwissLichens database currently contains more than 120’000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.    Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available.  They are free of charge for research projects and can be requested from InfoSpecies using a form.    Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.	proprietary
-synchrony_spongymoth_budburst_1.0	Synchrony between spongy moth hatching and leaf phenology of temperate trees	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083070-ENVIDAT.umm_json	"The files correspond to the data and R-script used for the analyses of the following paper ""Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate"" published in Ecology and Evolution by Vitasse et al. 2023.  There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses.    Input data:  'Synchrony_Cuttings_Pheno.txt':"	proprietary
 sys_etm_Not provided	Landsat 7 ETM+ Systematically Corrected (1999-May 2003)	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567695-USGS_LTA.umm_json	The USGS Earth Resources Observation and Science (EROS) Center archive holds data collected by the Landsat suite of satellites, beginning with Landsat 1 in 1972. All Landsat data held in the USGS EROS archive are available for download at no charge.   The Landsat archive provides a rich collection of information about the Earth's land surface. Major characteristics of changes to the surface of the planet can be detected, measured, and analyzed using Landsat data. The information obtainable from the historical and current Landsat data play a key role in studying surface changes through time. 	proprietary
 ta0704_1	CTD data from cruise ta0704 of the RV Tangaroa	AU_AADC STAC Catalog	2007-03-07	2007-03-29	158, -59.6, 167, -46	https://cmr.earthdata.nasa.gov/search/concepts/C1613498572-AU_AADC.umm_json	This is the CTD and Niskin bottle data set from the RV Tangaroa cruise tan0704, 7th Mar 2007 to 29th Mar 2007, along the Macquarie Ridge. This was the deployment cruise for the Macquarie Ridge mooring array. Dissolved oxygen data have been removed from this data set (oxygen bottle data never analysed). There were a total of 75 CTD casts on this cruise.	proprietary
 ta0803_1	CTD data from cruise ta0803 of the RV Tangaroa	AU_AADC STAC Catalog	2008-03-26	2008-04-26	158, -60, 166.5, -46	https://cmr.earthdata.nasa.gov/search/concepts/C1613498622-AU_AADC.umm_json	This is the CTD data set from RV Tangaroa cruise tan0803, 26th March to 26th April 2008, along the Macquarie Ridge. This was the recovery cruise for the Macquarie Ridge mooring array.  The primary aims of the oceanographic program were:  1. recovery of a New Zealand/Australia collaborative mooring array spanning two gaps in the Macquarie Ridge north of Macquarie Island, and  2. occupation of a CTD transect running south from New Zealand to 60o S along the Macquarie Ridge.  Eight of the nine moorings were successfully recovered. The mooring at site number 3 (NIWA gear) was unrecoverable, with acoustic release communication indicating only the bottom portion of the mooring remaining and lying flat on the ocean floor. Complete details of the mooring work are included in a separate mooring recovery report. Mooring instruments were downloaded on the ship, with a very high percentage of successful data recording. Ship maneouvering and deck operations all went well throughout the recoveries. Shiptime at the mooring locations was well spent, with daylight hours dedicated to mooring recovery, and night time used for nearby CTD, swath mapping, coring and sea mount activities, and for unspooling of mooring line. The additional container space created on the top deck portside (above the trawldeck) proved extremely valuable for stowage of mooring gear.   58 CTD's were completed during the cruise, including 54 along the main transect, and 4 at coring locations (part of the geology program). Main transect CTD's included 2 across the northern mooring group, and 3 across the southern mooring group. Most casts were to within 25 metres of the bottom. Instrument problems resulted in incomplete casts at the following locations: CTD 9, CTD 11 and CTD 27. CTD 46 was skipped due to bad weather, while further instrument problems prevented a cast at the southernmost site (CTD 50). Niskin bottles were sampled at each station for dissolved oxygen and salinity, with a subset of stations selected for 18O sampling. Some stations were additionally sampled for DIC, alkalinity, 13C, silicate, and U/Th, as part of the geology program.   Note that dissolved oxygen data have been removed from this data set, as oxygen bottle samples were never analysed.	proprietary
@@ -18874,8 +18157,6 @@ te6satns_328_1	BOREAS TE-06 1994 Soil and Air Temperatures in the NSA	ORNL_CLOUD
 te9bioav_339_1	BOREAS TE-09 Leaf Biochemistry Averages	ORNL_CLOUD STAC Catalog	1994-02-01	1994-09-18	-98.67, 55.84, -98.29, 55.91	https://cmr.earthdata.nasa.gov/search/concepts/C2808129316-ORNL_CLOUD.umm_json	Contains values of canopy biochemistry derived by the TE-09 team.  	proprietary
 te9biopd_340_1	BOREAS TE-09 Leaf Biochemistry Point Data	ORNL_CLOUD STAC Catalog	1994-02-01	1994-09-18	-98.67, 55.84, -98.29, 55.91	https://cmr.earthdata.nasa.gov/search/concepts/C2808129359-ORNL_CLOUD.umm_json	Contains sample measurements of the canopy biochemistry measured by the TE-09 team.  	proprietary
 te9spref_338_1	BOREAS TE-09 in situ Understory Spectral Reflectance within the NSA	ORNL_CLOUD STAC Catalog	1994-06-13	1994-07-31	-98.62, 55.9, -97.34, 56	https://cmr.earthdata.nasa.gov/search/concepts/C2808129283-ORNL_CLOUD.umm_json	Contains forest understory spectral reflectance data collected by BOREAS TE-09 at the ground level in the Old Jack Pine, Young Jack Pine nd Young Aspen boreal forest sites in the NSA.  	proprietary
-temperature-dependent-life-history-ips-typographus_1.0	Temperature-dependent development and oviposition of the spruce bark beetle Ips typographus	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	8.4557819, 47.3611898, 8.4557819, 47.3611898	https://cmr.earthdata.nasa.gov/search/concepts/C2789817829-ENVIDAT.umm_json	Ips typographus was reared in climate chambers at constant temperatures of 12, 15, 20, 25, 30 and 33°C. Developmental times from egg to teneral beetle stages and daily oviposition of females from preoviposition phase to their death were recorded. From these data life tables were computed and the data were used for modelling.	proprietary
-terrestrial-laser-scans-on-hammarryggen-ice-rise-dml-east-antarctica_1.0	Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	21.87, -70.5, 21.87, -70.5	https://cmr.earthdata.nasa.gov/search/concepts/C2789817876-ENVIDAT.umm_json	Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.    Notes:    * All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).    * From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.    * The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.    * By walking along fixed corridors, disturbance of the snow was limited.	proprietary
 tf01soil_511_1	BOREAS TF-01 SSA-OA Soil Characteristics Data	ORNL_CLOUD STAC Catalog	1994-02-02	1994-11-26	-106.2, 53.63, -106.2, 53.63	https://cmr.earthdata.nasa.gov/search/concepts/C2808092710-ORNL_CLOUD.umm_json	Data collected in support of the effort to characterize and interpret soil information at the SSA-OA tower site in 1994. Data collected include soil respiration, temperature, moisture, and gravimetric data.	proprietary
 tf01tflx_512_1	BOREAS TF-01 SSA-OA Tower Flux, Meteorological, and Soil Temperature Data	ORNL_CLOUD STAC Catalog	1996-02-02	1996-12-31	-106.2, 53.63, -106.2, 53.63	https://cmr.earthdata.nasa.gov/search/concepts/C2808092747-ORNL_CLOUD.umm_json	Energy, cargon dioxide, and momentum flux data collected above the canopy along with meteorological and soils data at the BOREAS SSA-OA site	proprietary
 tf01uflx_513_1	BOREAS TF-01 SSA-OA Understory Flux, Meteorological, and Soil Temperature Data	ORNL_CLOUD STAC Catalog	1993-10-12	1994-12-31	-106.2, 53.63, -106.2, 53.63	https://cmr.earthdata.nasa.gov/search/concepts/C2808092797-ORNL_CLOUD.umm_json	Energy, carbon dioxide, and momentum flux data collected under the canopy along with meteorological and soils data at the BOREAS SSA-OA site from mid-October to mid-November of 1993 and throughout all of 1994.	proprietary
@@ -18937,52 +18218,26 @@ tgb8scds_394_1	BOREAS TGB-08 Starch Concentration Data over the SSA-OBS and the
 tgb9nmhc_395_1	BOREAS TGB-09 Above-canopy NMHC at SSA-OBS, SSA-OJP and SSA-OA Sites	ORNL_CLOUD STAC Catalog	1994-05-27	1994-09-15	-106.2, 53.63, -104.69, 53.99	https://cmr.earthdata.nasa.gov/search/concepts/C2808132169-ORNL_CLOUD.umm_json	Contains the mixing ratio and concentration of Non-Methane HydroCarbons (NMHC) taken by the TGB-09 group.	proprietary
 tgbfenfx_378_1	BOREAS TGB-01/TGB-03 CH4 Chamber Flux Data: NSA Fen	ORNL_CLOUD STAC Catalog	1994-05-09	1996-10-21	-98.42, 55.92, -98.42, 55.92	https://cmr.earthdata.nasa.gov/search/concepts/C2808131382-ORNL_CLOUD.umm_json	Contains TGB-03 methane flux data for fens in the Northern Study Area.	proprietary
 tgbfenne_379_1	BOREAS TGB-01/TGB-03 NEE Data over the NSA Fen	ORNL_CLOUD STAC Catalog	1994-06-06	1996-10-23	-98.42, 55.92, -98.42, 55.92	https://cmr.earthdata.nasa.gov/search/concepts/C2808131414-ORNL_CLOUD.umm_json	Contains TGB-03 NET Ecosystem Exchange data from the combined TGB-01 and TGB-03 teams.	proprietary
-the-experimental-forest-management-network_1.0	The Experimental Forest Management network	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817900-ENVIDAT.umm_json	The EFM network is one of the longest running scientific projects in Switzerland and has been collecting growth and yield data since the late 1880’s. As of 2021, 28 plots had been monitored for at least 100 years and 81 for at least 75 years. The network is used to examine silvicultural treatments across a range of species, climate and edaphic conditions. There are currently 465 plots covering a total area of 148 hectares. Over the > 130-year history of the project, at least another 1000 plots were monitored and then deactivated after they reached their experimental goal (e.g. end of the rotation). The data from all 1480 plots are available for analyses.	proprietary
-the-origin_1.0	The origin of urban communities: from the regional species pool to community assemblages in city	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.4771538, 47.3551798, 8.5959435, 47.4070207	https://cmr.earthdata.nasa.gov/search/concepts/C2789817936-ENVIDAT.umm_json	Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland.    Dataset available upon request (An agreement between the data provider and the data recipient is necessary).	proprietary
-the-usage-of-landscape-ecological-concepts-in-the-planning-literature_1.0	The usage of landscape ecological concepts in the planning literature	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	180, -90, -180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2789817969-ENVIDAT.umm_json	Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.    The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?     We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:     &nbsp; 1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.    &nbsp; 2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.    &nbsp; 3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).   This data was used to produce the Figure 3a at the original publication.    &nbsp; 4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.       To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:    &nbsp; 5. Planning steps.  This data provides the number of times landscape ecological concepts were addressed in each   planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the   original publication.    &nbsp; 6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the   data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the   paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning   steps described above?	proprietary
-three-dimensional-debris-flow-simulation-tool-debrisintermixing_4.x; 6	Three-dimensional debris flow simulation tool debrisInterMixing	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818024-ENVIDAT.umm_json	Here the updated versions of debrisInterMixing are provided for download. The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., Hürlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017.  DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter.  For more information, please contact albrecht.vonboetticher@wasserbau.ch.     For a recent summary on applications see the DFHM8 contribution at https://www.e3s-conferences.org/articles/e3sconf/abs/2023/52/e3sconf_dfhm82023_02024/e3sconf_dfhm82023_02024.html - DOI: https://doi.org/10.1051/e3sconf/202341502024    UPDATE: DebrisInterMixing for OpenFOAM-7 is available, please contact albrecht.vonboetticher@wasserbau.ch. DebrisInterMixing with OpenFOAM-10 is ready but not yet validated.	proprietary
 timber_125_1	Timber Measurements (OTTER)	ORNL_CLOUD STAC Catalog	1990-08-01	1990-08-01	-123.92, 44.29, -121.33, 45.05	https://cmr.earthdata.nasa.gov/search/concepts/C2804789959-ORNL_CLOUD.umm_json	Height, crown width, DBH, and height-to-crown distance collected using variable-radius plot sampling with steel tape and hand-held compass to locate points along transect	proprietary
-time-series-data-on-dynamic-crack-propagation-in-long-propagation-saw-tests_1.0	Time series data on dynamic crack propagation in long propagation saw tests	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.869982, 46.8077169, 9.869982, 46.8077169	https://cmr.earthdata.nasa.gov/search/concepts/C3226083229-ENVIDAT.umm_json	This data set includes material and results described in the related research article:    Bergfeld, B., van Herwijnen A., Bobillier, G., Rosendahl P., Weißgraeber P., Adam V., Dual, J., and Schweizer, J.: Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and  sustained propagation, Natural Hazards and Earth System Sciences, 23, 293-315, https://doi.org/10.5194/nhess-23-293-2023, 2023.    We performed a series of propagation saw test experiments, up to ten meters long, over a period of 10 weeks and analyzed these using digital image correlation techniques. We derived the elastic modulus of the slab, the elastic modulus of the weak layer and the specific fracture energy of the weak layer with a homogeneous and a layered slab model. During crack propagation, we measured crack speed, touchdown distance and the energy dissipation due to compaction and dynamic fracture. Our data set provides unique insight and valuable data to validate models.	proprietary
 tims0bil_282_1	BOREAS Level-0 TIMS Imagery: Digital Counts in BIL Format	ORNL_CLOUD STAC Catalog	1994-04-16	1994-09-17	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2927623814-ORNL_CLOUD.umm_json	The TIMS imagery, along with the other remotely sensed images, was collected to provide spatially extensive information over the primary study areas.  This information includes detailed land cover, biophysical parameter maps such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI), and surface thermal properties.	proprietary
 tims1bsq_436_1	BOREAS Level-1B TIMS Imagery: At Sensor Radiance in BSQ Format	ORNL_CLOUD STAC Catalog	1994-04-16	1994-09-17	-106.32, 53.42, -97.23, 56.25	https://cmr.earthdata.nasa.gov/search/concepts/C2813517035-ORNL_CLOUD.umm_json	TIMS imagery, along with other aircraft images, was collected to provide spatially extensive information over the primary study areas.  The level-1B TIMS images cover the time periods of 16-Apr-1994 to 20-Apr-1994 and 06-Sep-1994 to 17-Sep-1994. 	proprietary
 tmiwop_3	TRMM MICROWAVE IMAGER (TMI) WENTZ OCEAN PRODUCTS V3	GHRC_DAAC STAC Catalog	2006-09-17	2015-04-03	-180, -38, 180, 38	https://cmr.earthdata.nasa.gov/search/concepts/C1979952419-GHRC_DAAC.umm_json	The TRMM Microwave Imager (TMI) Wentz Ocean Products dataset used the TRMM Microwave Imager (TMI), which is a 5-channel, dual-polarized, passive microwave radiometer. The TMI is used to measure several important meteorological parameters over sea surfaces, such as precipitation rate, wind speed, wapter vapor, and sea surface temperature. The TMI, a successor to the SSM/I, measures radiation at frequencies of 10.7, 19.4, 21.3, 37, 85.5 GHz. It orbits at an altitude of 218 miles, much lower than the SSM/I, thus providing better resolution.	proprietary
-topoclim-v-1-0-code_1.0	TopoCLIM v1.0 code	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818149-ENVIDAT.umm_json	Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.    A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.    License GPL v.3	proprietary
-topoclim-v1-1-code_1.1	TopoCLIM v1.1 code	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789816880-ENVIDAT.umm_json	Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.    A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.	proprietary
-torymus-sinensis-population-evolution-from-arrival-to-biocontrol_1.0	Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	7.1740723, 44.2197466, 9.4592285, 46.6099536	https://cmr.earthdata.nasa.gov/search/concepts/C2789817001-ENVIDAT.umm_json	This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.    The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.    The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.    These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions	proprietary
-total_basal_area-2_1.0	Total basal area	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817158-ENVIDAT.umm_json	Sum of the stem cross-section areas of all living and dead trees and shrubs starting at 12 cm dbh at a height of 1.3 m (dbh measurement height).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_basal_area_nfi1-238_1.0	Total basal area NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817277-ENVIDAT.umm_json	Sum of stem cross-section areas at a height of 1.3 m (dbh measurement height) of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method.  In NFI1 only those dead trees were recorded whose wood could still be exploited.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_stem_number-3_1.0	Total stem number	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817428-ENVIDAT.umm_json	Number of stems of all living and dead trees and shrubs starting at 12 cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_stem_number_by_cause_of_damage-218_1.0	Total stem number by cause of damage	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817559-ENVIDAT.umm_json	Number of all living and dead trees and shrubs  starting at 12 cm dbh where a particular cause of damage (including no damage, dead or lying) was determined. One tree may have damage with more than one type of origin, which means it may contribute to the total number of stems with damage with several different types of origin.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_stem_number_by_type_of_damage-208_1.0	Total stem number by type of damage	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817706-ENVIDAT.umm_json	Number of all living and dead trees and shrubs  starting at 12 cm dbh where a particular type of damage (including no damage, dead or lying) was observed. One tree may have more than one type of damage, which means it may contribute to the total number of stems for several different types of damage.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_stem_number_nfi1-243_1.0	Total stem number NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817822-ENVIDAT.umm_json	Number of stems of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_timber_volume-23_1.0	Total timber volume	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817860-ENVIDAT.umm_json	Volume of stemwood with bark of all living and dead trees and shrubs (standing and lying)  starting at 12 cm dbh. This corresponds to the sum of the volumes of growing stock and deadwood. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-total_timber_volume_nfi1-242_1.0	Total timber volume NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817882-ENVIDAT.umm_json	Volume of stemwood with bark of all living and dead trees and shrubs  starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 trace_metals_1	Iron, manganese and aluminium in East Antarctic snow and ice cores	AU_AADC STAC Catalog	1994-09-21	1998-03-31	75, -76, 180, -65	https://cmr.earthdata.nasa.gov/search/concepts/C1214311345-AU_AADC.umm_json	Iron, manganese and aluminium concentrations have been determined in modern and ancient East Antarctic snow from sea-ice and continental sites. Modern snow samples were collected from sites in Prydz Bay, Dumont d'Urville sea, Ross Sea and Princess Elizabeth Land. Trace metal concentrations in ancient snow were determined form ice cores drilled from Law Dome, Wilkes Land. The ice cores analysed included DSS, DEO8-1 and BHC1.  This work was completed as part of ASAC project 827 (ASAC_827).	proprietary
 trajectory_images_792_1	SAFARI 2000 Modeled Tropospheric Air Mass Trajectories, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-08-14	2000-09-23	17.1, -30, 31, -15.3	https://cmr.earthdata.nasa.gov/search/concepts/C2789730089-ORNL_CLOUD.umm_json	The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa for the period August 14, 2000 to September 23, 2000. These sites are Durban, Middleburg, Pietersburg, and Springbok, South Africa; Maun, Botswana; Mongu, Zambia; and Windhoek, Namibia. The twice daily three-dimensional wind field (at 0000 and 1200 UTC) was used as input to the trajectory model. By integrating the vertical motion of the air parcels over a period of time, the trajectory model was able to forecast the net vertical displacement of air parcels during 12-hour periods. The resulting trajectory plots represent the three-dimensional transport of air in time and can be used to examine what is happening in the low-to-mid troposphere during flight and ground-based observations. These levels are most significant in terms of the thermodynamic structure of the troposphere, especially the stable layers and accumulation of material between and below them, as well containing the major levels of subsidence over the subcontinent. The trajectory model output and  thermodynamic profiles of the troposphere were used to position aircraft for sampling trace gases, aerosols and other species during the SAFARI 2000 field campaign and to predict regions of high aerosol and trace gas concentrations downwind.The model output data are daily forward and backward trajectory plots at 850 hPa, 700 hPa, and 500 hPa pressure levels for each location. The plots are provided as JPEG images with coordinate, date, and time stamps.	proprietary
-tree-ring-data-earlybrowning-2018_1.0	Tree-ring data of European beech with premature leaf discoloration in 2018 and beech with normal leaf fall	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	7.2756958, 47.1903969, 9.2092896, 47.7990732	https://cmr.earthdata.nasa.gov/search/concepts/C3226083107-ENVIDAT.umm_json	"Tree-ring data and tree location from 470 European beech trees (Fagus sylvatica L.) located in the northern part of Switzerland.  278 trees showed drought-induced premature leaf discoloration and shedding in summer 2018 and 192 showed normal leaf fall. The trees were selected from the ""1000-Beech-Project"" published by Frei et al. 2022  and the data was analyzed in Neycken et al 2023 (in preparation). The corresponding crown data are archived in the EnviDat data portal https://doi.org/10.16904/envidat.422 (Frei et al. 2023). All other data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.    Publications related to original data set and crown data:    Wohlgemuth, T., Kistler, M., Aymon, C., Hagedorn, F., Gessler, A., Gossner, M.M., Queloz, V., Vögtli, I., Wasem, U., Vitasse, Y., Rigling, A., 2020. Früher Laubfall der Buche während der Sommertrockenheit 2018: Resistenz oder Schwächesymptom? Schweizerische Zeitschrift fur Forstwesen 171, 257–269. https://doi.org/10.3188/szf.2020.0257    Frei, E.R., Gossner, M.M., Vitasse, Y., Queloz, V., Dubach, V., Gessler, A., Ginzler, C., Hagedorn, F., Meusburger, K., Moor, M., Samblás Vives, E., Rigling, A., Uitentuis, I., von Arx, G., Wohlgemuth, T., 2022. European beech dieback after premature leaf senescence during the 2018 drought in northern Switzerland. Plant Biol J 24, 1132–1145. https://doi.org/10.1111/plb.13467      Publication related to tree-ring data and growth analysis:  Neycken et al 2023 (in preparation)"	proprietary
-tree-ring-laser-ablation-data_1.0	Tree-ring laser ablation data	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	125.8374023, 45.0507576, 128.5620117, 46.46107	https://cmr.earthdata.nasa.gov/search/concepts/C3226083113-ENVIDAT.umm_json	This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.    In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which  derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.    Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.	proprietary
-tree-rings-and-climate-data-of-four-tree-species-in-switzerland_1.0	Tree rings and climate data of four tree species in Switzerland	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817967-ENVIDAT.umm_json	Raw tree ring data and climate used in the following paper:  Vitasse Y, Bottero A, Cailleret M et al. (2019) Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob Chang Biol, 25, 3781-3792.	proprietary
 tree_cover-1km_641_1	SAFARI 2000 Tree Cover from AVHRR, 1-km, 1992-1993 (DeFries et al.)	ORNL_CLOUD STAC Catalog	1992-01-01	1993-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2804824511-ORNL_CLOUD.umm_json	This data set consists of a southern African subset of the 1-km Global Tree Cover Data Set developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Data are available in both ASCII GRID and binary image files formats.	proprietary
 trichonesia_0	Trichonesia cruise	OB_DAAC STAC Catalog	1998-03-26		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360686-OB_DAAC.umm_json	Measurements from Indonesia made during 1998.	proprietary
-trichopria_drosophilae_nuclear_microsats_1.0	Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.4492188, 45.4870947, 11.0522461, 48.1770756	https://cmr.earthdata.nasa.gov/search/concepts/C2789818012-ENVIDAT.umm_json	# Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_    This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).  Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.	proprietary
 trichototo_0	Trichodesmium Toto (TRICHOTOTO) cruise	OB_DAAC STAC Catalog	1999-11-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1633360687-OB_DAAC.umm_json	Measurements from the North Australian Coast in the Timur and Arafura Seas in 1999.	proprietary
 trmlbalip_1	TRMM-LBA LIGHTNING INSTRUMENT PACKAGE (LIP) V1	GHRC_DAAC STAC Catalog	1999-01-22	1999-02-24	-63.4124, -16.0234, -47, -9	https://cmr.earthdata.nasa.gov/search/concepts/C1979956303-GHRC_DAAC.umm_json	The TRMM-LBA Lightning Instrument Package (LIP) dataset consists of electrical field measurements of lightning from eight field mills, conductivity probe temperatures from two probes, and navigation data, for the period of January 22 through February 24, 1999. These data were collected by the LIP instrument flown aboard the NASA ER-2 high-altitude aircraft over the Amazon River basin in Brazil during the Tropical Rainfall Measuring Mission-Large-Scale Biosphere-Atmosphere Experiment in Amazonia (TRMM-LBA) field campaign. The LIP instrument was used to validate measurements by the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS). These data are provided in HDF-4 format with browse imagery available in GIF format. 	proprietary
 trmmtcpfl1_1	TRMM TROPICAL CYCLONE PRECIPITATION FEATURE (TCPF) DATABASE - LEVEL 1 V1	GHRC_DAAC STAC Catalog	1997-12-08	2011-12-30	-179.98, -39.14, 180, 39.11	https://cmr.earthdata.nasa.gov/search/concepts/C1983255369-GHRC_DAAC.umm_json	The TRMM Cyclone Precipitation Feature (TCPF) Database - Level 1 provides Tropical Rainfall Measuring Mission (TRMM)-based tropical cyclone data in a common framework for hurricane science research. This dataset aggregated observations from each of the TRMM instruments for each satellite orbit that was coincident with a tropical cyclone in any of the six TC-prone ocean basins. These swath data were co-located and subsetted to a 20-degree longitude by 20-degree latitude bounding box centered on the tropical storm, which is typically large enough to observe the various sizes of TCs and their immediate environments. The TCPF Level 1 dataset was created by researchers at Florida International University (FIU) and the University of Utah (UU) from the UU TRMM Precipitation Feature database. The TCPF database was built by extracting those precipitation features that are identified as tropical cyclones (TC) using the TC best-track data provided by National Hurricane Center or the US Navy's Joint Typhoon Warning Center.	proprietary
-tschamut2014_1.0	Repetitive trajectory testing in Tschamut 2014	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	8.7003458, 46.6526428, 8.7028348, 46.6545575	https://cmr.earthdata.nasa.gov/search/concepts/C2789818065-ENVIDAT.umm_json	In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.    An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.     The dataset contains all data that were gathered through above field campaign.	proprietary
-turbulence-patchy-snow-cover_1.0	Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	9.9224997, 46.7196131, 9.9224997, 46.7196131	https://cmr.earthdata.nasa.gov/search/concepts/C3226083051-ENVIDAT.umm_json	"This dataset contains the raw data that is analyzed in the publication entitled ""Turbulence in The Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow"". Please find information on the individual data files in the description of the files. The data was recorded during a comprehensive field campaign in May and June 2021 at Dürrboden at the end of Dischma valley close to Davos (Graubünden, CH)."	proprietary
-twig_mass_of_live_trees-48_1.0	Twig mass of live trees	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817079-ENVIDAT.umm_json	Dry weight (mass) of branches with a diameter under 7 cm from living trees and shrubs  starting at 12cm dbh.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 twin_pups_1	Apparent twin pups of the weddell seal near Mawson, Antarctica	AU_AADC STAC Catalog	1976-10-09	1976-10-24	62.5, -67.5, 63, -67.3	https://cmr.earthdata.nasa.gov/search/concepts/C1214311346-AU_AADC.umm_json	Phocid seals give birth annually, generally to a single pup.  Twins have been reported occasionally, either from observations made in utero or from observations of live pups in the field.  Examples of the former are reports of two embryos in a Weddell seal, Leptonychotes weddellii and of twin foetuses of a southern elephant seal, Mirounga leonina.  Observations of two pups suckling one adult female have been reported for weddell seals.  For southern elephant seals, an adult female that expelled two placentae and gave birth to a pup while another newborn pup was nuzzling the female, has also been reported. Here we use the expression 'apparent twins' to refer to reports of twin weddell seal pups that are based solely on field observations of two pups with the same adult female on several occasions.  The data arising from this study has been recorded in the form of 3 observations.  These observations can be found in the referenced paper.  A copy of this paper is available for download as a pdf document from the provided URL.	proprietary
 ualbmrr2impacts_1	UAlbany Micro Rain Radar 2 (MRR-2) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-30	2023-02-28	-73.832439, 42.6803769, -73.8139065, 42.6862804	https://cmr.earthdata.nasa.gov/search/concepts/C2382050573-GHRC_DAAC.umm_json	The UAlbany Micro Rain Radar 2 (MRR-2) IMPACTS dataset consists of reflectivity, Doppler velocity, signal-to-noise ratio, spectral width, droplet size, Liquid Water Content, melting layer, drop size distribution, rain attenuation, rain rate, and radial velocity data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The MRR-2 instrument was used to collect data for this dataset. The dataset files are available from January 30, 2020, through February 28, 2023, in netCDF-3 and netCDF-4 format.	proprietary
 ualbparsimpacts_1	UAlbany Parsivel IMPACTS	GHRC_DAAC STAC Catalog	2020-01-30	2023-02-28	-73.8419, 42.6709158, -73.8044455, 42.6957	https://cmr.earthdata.nasa.gov/search/concepts/C2102858144-GHRC_DAAC.umm_json	The UAlbany Parsivel IMPACTS dataset consists of precipitation data collected by a Parsivel2 disdrometer in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Parsivel disdrometer data include particle size distribution, fall speed, radar reflectivity, and precipitation rate. The dataset files are available in netCDF-4 format from 30 January 2020 through 28 February 2023.	proprietary
 ualbsndimpacts_1	UAlbany Soundings IMPACTS	GHRC_DAAC STAC Catalog	2023-01-05	2023-03-01	-73.851, 42.549, -72.286, 43.621	https://cmr.earthdata.nasa.gov/search/concepts/C3065993627-GHRC_DAAC.umm_json	The UAlbany Soundings IMPACTS dataset consists of data measured with the iMet-3050A sounding system using 200-g meteorological balloons during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The UAlbany Soundings IMPACTS dataset consists of atmospheric pressure, relative humidity, mixing ratio, wind speed, and wind direction measurements. These data are available from January 5, 2023, through March 1, 2023, in ASCII format.	proprietary
-uas-based-snow-depth-maps-bramabuel-davos-ch_1.0	UAS based snow depth maps Brämabüel, Davos, CH	ENVIDAT STAC Catalog	2016-01-01	2016-01-01	9.8459816, 46.7767433, 9.8551011, 46.782768	https://cmr.earthdata.nasa.gov/search/concepts/C2789817199-ENVIDAT.umm_json	This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.    A detailed description is given here:    Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.    Abstract:  Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.   Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.	proprietary
-uav-datasets-for-three-alpine-glaciers_1.0	UAV-derived Digital Surface Models and orthoimages for three alpine glaciers	ENVIDAT STAC Catalog	2017-01-01	2017-01-01	7.8153698, 45.9888032, 8.6115619, 46.6089963	https://cmr.earthdata.nasa.gov/search/concepts/C2789817320-ENVIDAT.umm_json	### UAV-derived DSMs and orthoimages    Unmanned Aerial Vehicle  (UAV) surveys were conducted between 2015 and 2016 on the __Sankt Annafirn__, __Findelen-__ and __Griesgletscher__, situated in the __Swiss Alps__. Three surveys at the Sankt Annafirn allowed for a full glacier coverage,  four  surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a __high resolution orthoimage__ and a __Digital Surface Model (DSM)__ was created.    ### UAV surveys:    Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 .    The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area.    A folder for each dataset is available (see folder name in table), which contains:    - An orthoimage  __*glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*__    - A Digital Surface Model  __*glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*__    - The Agisoft Photoscan automatic processing report   __*glacier_date_photoscan_report.pdf*__    where:    - __*glacier*__ is the name of the surveyed glacier    - __*date*__ is the date of the UAV image acquisition    - __*photoscan*__ is the name of the photogrammetric software    - __*oi*__ or __*dsm*__ the type of dataset    - __*CH1903+_LV95*__ is the coordinate system and datum of the dataset    - __*0.1m*__ is the resolution of the dataset in meter    - __*.tif*__ is the extention of the dataset    &nbsp;    Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below.    __Paper Citation:__   > _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’Imagery on Glaciers, Remote Sensing, 9, 186, 1-15,  [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._    The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.	proprietary
 uiucsndimpacts_1	Mobile UIUC Soundings IMPACTS V1	GHRC_DAAC STAC Catalog	2020-01-18	2023-02-28	-88.253, 38.958, -70.661, 44.707	https://cmr.earthdata.nasa.gov/search/concepts/C1995869868-GHRC_DAAC.umm_json	The Mobile UIUC Soundings IMPACTS dataset consists of atmospheric sounding data collected by rawinsondes launched during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. These data include vertical profiles of atmospheric temperature, relative humidity, pressure, wind speed, and wind direction. Specifically, these rawinsondes were provided by the University of Illinois at Urbana-Champaign (UIUC). IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands;  and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The sounding data files are available in netCDF-4 format from January 18 through February 25, 2022, though it should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign.	proprietary
 uk_met_c-130_720_1	SAFARI 2000 C-130 Aerosol and Meteorological Data, Dry Season 2000	ORNL_CLOUD STAC Catalog	2000-09-05	2000-09-16	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788398494-ORNL_CLOUD.umm_json	The Met Office C-130 research aircraft was based at Windhoek, Namibia, between September 5-16, 2000, where it conducted a series of flights over Namibia as part of the SAFARI 2000 Dry Season Aircraft Campaign. The aims of the Met Office's research were as follows: (1) In-situ measurements of the physical, chemical and optical properties of the aerosol. The data set includes aerosol samples ranging from near source regions to aged plumes several hundreds of kilometres from source, some of which have been cloud processed. (2) Investigation of the direct radiative impact of aerosol over sea, land and low-level cloud. (3) In-situ measurements of aerosol properties in conjunction with ground-based sites to validate the ground-based retrievals of, for example, aerosol size distributions. (4) In-situ measurements of aerosol properties in conjunction with TERRA overpasses, in order to validate the satellite-based retrievals of aerosol properties. (5) In-situ measurements of stratus/stratocumulus cloud of Namibia/Angola in conjunction with TERRA overpasses, in order to validate satellite-based retrievals of cloud properties.	proprietary
 umd_landcover_xdeg_969_1	ISLSCP II University of Maryland Global Land Cover Classifications, 1992-1993	ORNL_CLOUD STAC Catalog	1992-04-01	1993-03-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784890869-ORNL_CLOUD.umm_json	The objective of the International Satellite Land Surface Climatology Project (ISLSCP II) study that produced this data set, ISLSCP II University of Maryland Global Land Cover Classifications 1992-1993, was to create a land cover map derived from 1 kilometer Advanced Very High Resolution Radiometer (AVHRR) data using all available bands, derived Normalized Difference Vegetation Index (NDVI), and a full year of data (April 1992-March 1993). This thematic map was resampled to 0.25, 0.5 and 1.0 degree grids. During this re-processing, the original University of Maryland (UMD) land cover type and fraction maps were adjusted to match the water/land fraction of the ISLSCP II land/water mask. These maps were generated for use by modelers of global biogeochemical cycles and others in need of an internally consistent, global depiction of land cover. This 1km map was also one of the Moderate resolution Imaging Spectroradiometer (MODIS) at-launch land cover maps. This product describes the geographic distributions of 13 classes of vegetation cover (plus water and unclassified classes) based on a modified International Geosphere-Biosphere Programme (IGBP) legend. The data set also provides the fraction of each of the 15 classes within the coarser resolution cells, at three spatial resolutions of 0.25, 0.5 and 1.0 degrees in latitude and longitude. 	proprietary
 und_refl_304_1	BOREAS RSS-19 1994 Seasonal Understory Reflectance Data	ORNL_CLOUD STAC Catalog	1994-02-06	1994-09-16	-105.12, 53.8, -98.29, 55.93	https://cmr.earthdata.nasa.gov/search/concepts/C2807645574-ORNL_CLOUD.umm_json	Average spectral reflectance measurements of the ground surface of BOREAS flux tower sites. Measurements made along a transect with the instrument held at approximately one meter above the ground.	proprietary
 unep_marineturtle_Not provided	Marine Turtle Nesting Database	CEOS_EXTRA STAC Catalog	1970-01-01		20, -39, 165, 39	https://cmr.earthdata.nasa.gov/search/concepts/C2232849059-CEOS_EXTRA.umm_json	Distribution of marine turtles in the Indian Ocean. Information was obtained from published and unpublished literature, and through liaison with turtle fieldworkers. It was intended that the database would be of use to a wide audience, including biologists, coastal planners and those concerned with emergency response to oil spills.  Assessing the level of demand for these data, and the feasibility of maintaining data to reflect best available information.	proprietary
-urals-latitudinal-decline-in-treeline-biomass-and-productivity_1.0	Urals: latitudinal decline in treeline biomass and productivity	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	52.9101563, 46.5830301, 74.7070313, 71.42438	https://cmr.earthdata.nasa.gov/search/concepts/C2789817471-ENVIDAT.umm_json	1. Stand characteristics of treeline ecotone along 18 elevational gradients of the Ural mountains.   2. Extrapolated climate data at treeline using nearby meteo station (1976-2006).  3. Air and soil temperatures measured in situ at treeline in the South and Polar Urals. Soil temperature sensors were placed at 10 cm depth in open areas in between tree clusters but not under tree canopy.  4. Further plot specific information is available upon request.	proprietary
 urn:eop:VITO:CGS_S1_GRD_L1_V001	Sentinel-1 Level-1 Ground Range Detected (GRD) products.	FEDEO STAC Catalog	2015-07-06	2021-12-31	-180, -84, 180, 84	https://cmr.earthdata.nasa.gov/search/concepts/C2207478611-FEDEO.umm_json	Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model such as WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Ground range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution. The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. For the IW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarisation.	proprietary
 urn:eop:VITO:CGS_S1_GRD_SIGMA0_L1_V001	Sentinel-1 Level-1 Ground Range Detected (GRD) SIGMA0 products.	FEDEO STAC Catalog	2014-10-03	2029-10-03	-1.0589251, 47.6603055, 11.6780987, 53.6748736	https://cmr.earthdata.nasa.gov/search/concepts/C2207478517-FEDEO.umm_json	The Sigma0 product describes how much of the radar signal that was sent out by Sentinel-1 is reflected back to the sensor, and depends on the characteristics of the surface. This product is derived from the L1-GRD product. Typical SAR data processing, which produces level 1 images such as L1-GRD product, does not include radiometric corrections and significant radiometric bias remains. Therefore, it is necessary to apply the radiometric correction to SAR images so that the pixel values of the SAR images truly represent the radar backscatter of the reflecting surface. The radiometric correction is also necessary for the comparison of SAR images acquired with different sensors, or acquired from the same sensor but at different times, in different modes, or processed by different processors. For this Sigma0 product, radiometric calibration was performed using a specific Look Up Table (LUT) that is provided with each original GRD product. This LUT applies a range-dependent gain including the absolute calibration constant, in addition to a constant offset. Next to calibration, also orbit correction, border noise removal, thermal noise removal, and range doppler terrain correction steps were applied during production of Sigma0. The terrain correction step is intended to compensate for distortions due to topographical variations of the scene and the tilt of the satellite sensor, so that the geometric representation of the image will be as close as possible to the real world.	proprietary
 urn:eop:VITO:CGS_S1_SLC_L1_V001	Sentinel-1 Level-1 Single Look Complex (SLC) products.	FEDEO STAC Catalog	2015-07-06	2021-12-31	-180, -84, 180, 84	https://cmr.earthdata.nasa.gov/search/concepts/C2207478525-FEDEO.umm_json	Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track. The products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The Interferometric Wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution (single look). IW mode captures three sub-swaths using Terrain Observation with Progressive Scans SAR (TOPSAR). IW SLC products contain one image per sub-swath and one per polarisation channel, for a total of three (single polarisation) or six (dual polarisation) images in an IW product. Each sub-swath image consists of a series of bursts, where each burst has been processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image with black-fill demarcation in between. There is sufficient overlap between adjacent bursts and between sub-swaths to ensure continuous coverage of the ground as provided in GRD products. The images for all bursts in all sub-swaths are resampled to a common pixel spacing grid in range and azimuth while preserving the phase information.	proprietary
@@ -19131,13 +18386,10 @@ usgsbrdnpwrcd00000012_Version 31JUL97	Changes in Breeding Bird Populations in No
 usgsbrdnpwrcd0000001_Version 15DEC98	An Assessment of Exotic Plant Species of Rocky Mountain National Park	CEOS_EXTRA STAC Catalog	1987-01-01	1991-01-01	-106, 40, -105, 40	https://cmr.earthdata.nasa.gov/search/concepts/C2231551467-CEOS_EXTRA.umm_json	"The invasion of exotic plants is becoming a problem in many ecosystems including some areas in Rocky Mountain National Park (RMNP) (Rocky Mountain National Park Resource Management Reports #1 and #13). Some exotic species, such as leafy spurge and spotted knapweed, are capable of rapidly colonizing areas, altering community composition, and even displacing native species (Belcher and Wilson 1989, Tyser and Key 1988). In many cases, the processes of invasion are poorly documented, and little information is available on an area's past history. However, there is a large amount of information available in the literature which relates to the life history traits of exotic species and the  distribution of exotic species. This information can be used to help predict the potential distribution and threat of exotic species to ecosystems.   Exotic plants can be thought of as those plants which did not originally occur in the ecosystem, and have since been introduced to the area. The National Park Service (NPS) defines an exotic species as, ""those that occur in a given place as a result of direct or indirect, deliberate, or accidental actions by humans."" This somewhat conservative definition of exotic species is necessary to insure that natural resources in national parks are preserved.   NPS policy generally prohibits the introduction of exotic species into natural areas of national parks. Exotic species which threaten park resources or public health are to be managed or eliminated if possible. In addition, the NPS recently signed a memorandum of understanding with 10 other federal and state agencies in the state of Colorado. This agreement states that all paid management agencies will work with private and county entities to manage exotic plants and, in particular, ""noxious weeds."" RMNP is currently working with Estes Park in exotic plant control as part of this agreement."	proprietary
 usgsbrdnpwrcd0000003_Version 16JUL97	Human Disturbances of Waterfowl: An Annotated Bibliography.	CEOS_EXTRA STAC Catalog	1970-01-01		-177.1, 13.71, -61.48, 76.63	https://cmr.earthdata.nasa.gov/search/concepts/C2231551896-CEOS_EXTRA.umm_json	The expansion of outdoor recreational activities has increased greatly the interaction between the public and waterfowl and waterfowl habitat. The effects of these interactions on waterfowl habitats are more visible and obvious, whereas the effects of interactions which disrupt the normal behavior of waterfowl are more subtle and often overlooked, but perhaps no less of a problem than destruction of habitat.  This bibliography contains excerpts or annotations from 211 articles that contained information about effects of human disturbances on waterfowl. Indices are provided for subject/keywords, geographic locations, species of waterfowl, and authors used in this bibliography.	proprietary
 usgsbrdnpwrcs0000004_Version 12MAY03	Collecting and Analyzing Data from Duck Nesting Studies	CEOS_EXTRA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2231554360-CEOS_EXTRA.umm_json	Northern Prairie has a long history of studying nest success of upland nesting ducks. Over the years, we have developed standardized procedures for collecting and analyzing these types of data. Data forms and instruction manuals developed by the Center are used widely by biologists throughout the northern Great Plains and elsewhere. Extensive use of standardized procedures led to a cooperative effort among Federal, State, Private, and other Non-Government Organizations that has allowed us to compile the Nest File, a data base of more than 75,000 duck nests spanning 30+ years in the northern Great Plains.	proprietary
-validation-of-the-critical-crack-length-in-snowpack_1.0	Validating and improving the critical crack length in SNOWPACK	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.78797, 46.80757, 9.809407, 46.8292944	https://cmr.earthdata.nasa.gov/search/concepts/C2789817607-ENVIDAT.umm_json	To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.   These data were analyzed and results were published in Richter et al. (2019).    Please refer to the Readme file for further details on the data.    These data are the basis of the following publication:  Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.	proprietary
 vanderford_data_1983_85_1	Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85	AU_AADC STAC Catalog	1983-01-01	1985-12-31	108, -67.5, 113, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311394-AU_AADC.umm_json	A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results.  These documents have been archived at the Australian Antarctic Division.	proprietary
 vanderford_gravity_1980_1	Gravity Readings, Vanderford Glacier 1980	AU_AADC STAC Catalog	1980-02-11	1980-02-15	110, -67.5, 112, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311395-AU_AADC.umm_json	A collection of gravity readings, taken on the Vanderford Glacier in February 1980. Also includes barometric pressure readings, taken at the same time, for determining the height of the location where the reading was taken.  Physical copies of these documents have been stored in the Australian Antarctic Division records store.	proprietary
-vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0	Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	174.375, -84.9479651, -179.546875, -42.7168763	https://cmr.earthdata.nasa.gov/search/concepts/C3226083103-ENVIDAT.umm_json	# Summary    This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.    # Main model characteristics    The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.    ### Model Sublimation  Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.    ### Model Air Parcel  Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.    # References    Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6    Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705	proprietary
 veg_continuous_fields_xdeg_931_1	ISLSCP II Continuous Fields of Vegetation Cover, 1992-1993	ORNL_CLOUD STAC Catalog	1992-04-01	1993-05-31	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2784863182-ORNL_CLOUD.umm_json	The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.	proprietary
 vegdri_Not provided	Vegetation Drought Response Index (VegDRI)	USGS_LTA STAC Catalog	1970-01-01		-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C1220567914-USGS_LTA.umm_json	The National Drought Mitigation Center produces VegDRI in collaboration with the US Geological Survey's (USGS) Center for Earth Resources Observation and Science (EROS), and the High Plains Regional Climate Center (HPRCC), with sponsorship from the US Department of Agriculture's (USDA) Risk Management Agency (RMA). Main researchers working on VegDRI are Dr. Brian Wardlow and Dr. Tsegaye Tadesse at the NDMC, and Jesslyn Brown with the USGS, and Dr. Yingxin Gu with ASRC Research and Technology Solutions, contractor for the USGS at EROS.  VegDRI maps are produced every two weeks and provide regional to sub-county scale information about drought's effects on vegetation. In 2006, VegDRI covered seven states in the Northern Great Plains (CO, KS, MT, NE, ND, SD, and WY). It expanded across eight more states in 2007 to cover the rest of the Great Plains (NM, OK, MO, and TX) and parts of the Upper Midwest (IA, IL, MN, and WI). VegDRI expanded to include the western U.S. in 2008 (WA, ID, OR, UT, CA, AZ, NV). In May 2009 VegDRI began depicting the eastern states as well, covering the entire conterminous 48-state area.	proprietary
-vegetation-height-model-nfi_2019 (current)	Vegetation Height Model NFI	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.9161377, 45.7406934, 10.5743408, 47.8574029	https://cmr.earthdata.nasa.gov/search/concepts/C2789817832-ENVIDAT.umm_json	A national vegetation height model was calculated for Switzerland using digital aerial images.  We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution  (1 × 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual  vegetation heights using a digital terrain model (DTM)  based on laser data with the buildings masked out, and to  produce a vegetation height model (VHM). Such a model  will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very  high level of detail. For covering the whole of Switzerland,  we use summer aerial images from six years.    Latest version is from 2019.	proprietary
 vegsoils_wilhend_642_1	SAFARI 2000 Vegetation and Soils, 1-Deg (Wilson and Henderson-Sellers)	ORNL_CLOUD STAC Catalog	1900-01-01	1999-12-31	5, -35, 60, 5	https://cmr.earthdata.nasa.gov/search/concepts/C2788349752-ORNL_CLOUD.umm_json	This data set contains a subset for southern Africa of Wilson and Henderson-Sellers' Global Vegetation and Soils 1-degree data. The data are available in both ASCII GRID and binary image file formats.	proprietary
 vemap-1_VEMAP1_CDROM_566_1	VEMAP 1: Model Input Database CD-ROM ISO Image	ORNL_CLOUD STAC Catalog	1895-01-01	1993-12-31	-124.5, 25, -67, 49	https://cmr.earthdata.nasa.gov/search/concepts/C2945353865-ORNL_CLOUD.umm_json	The VEMAP 1: Model Input Database CD-ROM ISO image contains long-term data that were used as input in comparing models during Phase 1 of the Vegetation Ecosystem Modeling and Analysis Project. Compiled and model-generated data sets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States.  Dates of the data sets range between 1895 and 1996.  The data are gridded at 0.5 degree latitude by 0.5 degree longitude.	proprietary
 vemap-1_VEMAP_Alaska_1344_1	VEMAP 2: Monthly Historical and Future Climate Data, Alaska, USA	ORNL_CLOUD STAC Catalog	1922-01-01	2100-12-31	-170.5, 53.5, -128.5, 71.5	https://cmr.earthdata.nasa.gov/search/concepts/C2945392606-ORNL_CLOUD.umm_json	This data set provides the results of the development of The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) Phase 2 transient climate change scenarios for the state of Alaska, USA. The data include gridded monthly historical and future estimates of maximum and minimum temperature, solar radiation, vapor pressure, irradiance, relative humidity and potential evapotranspiration at 0.5-degree spatial resolution. Historical data are for the period 1922-1996; future estimates cover the period 1997-2100.	proprietary
@@ -19159,50 +18411,18 @@ vemap-2_results_monthly_767_1	VEMAP 2: Monthly Ecosystem Model Responses to U.S.
 vest_lake_samples_gis_1	Locations of samples from Organic Lake, Deep Lake and Ellis Fjord in the Vestfold Hills, Antarctica	AU_AADC STAC Catalog	1975-01-01	1989-12-31	77.84088, -68.75729, 78.85986, -68.4012	https://cmr.earthdata.nasa.gov/search/concepts/C1214311352-AU_AADC.umm_json	This data set contains locations of sample sites for Ellis Fjord (1989), Organic Lake (1985) and Deep Lake (1975, 1975) in the Vestfold Hills.     Unfortunately little is known as to what samples were collected.  It is believed that water samples were taken at all locations, and that bottom sediment samples were taken at least at Deep Lake.  When questioned in 2009, the investigating scientist was unable to remember exactly what work was done.  The original maps may provide some clues.	proprietary
 vestfold_hills_dem_1	Digital Elevation Model of Vestfold Hills, Antarctica	AU_AADC STAC Catalog	1994-11-02	1997-02-11	77.788, -68.7378, 78.6972, -68.3364	https://cmr.earthdata.nasa.gov/search/concepts/C1214314093-AU_AADC.umm_json	A Digital Elevation Model (DEM) of the Vestfold Hills with cell size 10 metres was interpolated from input coastline, contour, spot height (point locations with an elevation attribute) and lake data from the dataset described by the metadata record 'Vestfold Hills 1:25000 Topographic GIS Dataset' with Entry ID: vest_hills_gis. The contour data is estimated to have horizontal accuracy of about 12 metres and vertical accuracy of about 5 metres. The spot heights are estimated to have horizontal accuracy of about 2 metres and vertical accuracy of about 1 metre. The interpolation was done using the Topo to Raster tool in ArcGIS. In the interpolation process all cells within a lake are assigned to the minimum elevation value of all cells along the shoreline. i.e. the interpolation is flat across the lake. The output DEM was clipped to the extents of the input data. The dataset available from a Related URL in this metadata record includes a text file with the parameters used with the Topo to Raster tool. The DEM is stored in the UTM Zone 44S projection. The horizontal datum is WGS84. The vertical datum is Mean Sea Level. The DEM was initially created as a raster in an ESRI file geodatabase. The geodatabase also includes slope, aspect and hillshade rasters derived from the DEM using ArcGIS. Slope is in degrees. Azimuth 315 degrees and altitude 45 degrees were chosen for the hillshade. The DEM was exported using ArcGIS to two other formats which are included in the dataset available from a Related URL in this metadata record: 1 A tiff, georeferenced with a world file; and  2 An ascii file in ESRI's ascii format for rasters.	proprietary
 vestfold_seals_gis_1	GIS data derived from 'Distribution of Weddell seals pupping at the Vestfold Hills' dataset	AU_AADC STAC Catalog	1973-09-30	1999-03-31	77.7925, -68.7028, 78.5775, -68.3472	https://cmr.earthdata.nasa.gov/search/concepts/C1214311440-AU_AADC.umm_json	This dataset represents Weddell Seal haulout and pupping sites in the Vestfold Hills, Antarctica. The data were sourced from a dataset compiled by Samantha Lake and described by the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'. She used a reporting grid described by the metadata record 'Weddell seal reporting grid of the Vestfold Hills, Antarctica' to show observations made over 24 years (pupping areas) and 28 years (non-breeding areas). The map Samantha produced of pupping areas is linked to the metadata record 'Distribution of Weddell seals pupping at the Vestfold Hills'.  Polygons were generated by copying relevant grid rectangles from a digital version of the reporting grid, referring to the maps produced by Samantha; the grid rectangles used were those in which there had been greater than 20 observations (pupping), 17 observations (non-breeding). The data was used in an A3 map of the Vestfold Hills published by the Australian Antarctic Data Centre in October 2001 and which is available from a Related URL below.  The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below.  Data described by this metadata record has Dataset_id = 155.  Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.	proprietary
-vineyard-plots-in-southern-switzerland_1.0	Vineyard plots in southern Switzerland	ENVIDAT STAC Catalog	2023-01-01	2023-01-01	8.3811951, 45.8221902, 9.2930603, 46.6202927	https://cmr.earthdata.nasa.gov/search/concepts/C3226083115-ENVIDAT.umm_json	Geospatial vector data (shapefile) representing the cadastral plots in the Canton Ticino and the Moesa region (southern Switzerland) having a part of the surface occupied by vineyards in the years 1989 and/or 2020 according to the corresponding edition of the Swiss national topographic maps in the scale 1:25,000 and to the topographic landscape model of Switzerland swissTLM3D (Federal office of topography Swisstopo). In the attribute table there is many variables which describe the topography of the site, the characteristics of the plots and the evolution of the wine growing area inside the plot between 1989 and 2020. Coordinate system: EPSG:2056 - Swiss CH1903+ / LV95.	proprietary
-volume-21_1.0	Volume	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817894-ENVIDAT.umm_json	"Volume of stemwood with bark of living trees and shrubs (standing and lying)  starting at 12 cm dbh. This corresponds internationally to the ""growing stock"". The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_"	proprietary
-volume_of_bole_wood_hg_2000-167_1.0	Volume of bole wood (HG 2000)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817935-ENVIDAT.umm_json	Wood volume of the stem without bark or stump at least 7 cm in diameter (limit of coarse wood) of all trees and shrubs  starting at 12 cm dbh, based on the stem-form functions according to Kaufmann (2001). The definition of the assortment is based on the 2000 edition of the Trading Practices (Handelsgebräuchen Ausgabe 2000 ).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-volume_of_bole_wood_hg_2010-211_1.0	Volume of bole wood (HG 2010)	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817965-ENVIDAT.umm_json	Wood volume of the trunk without bark or branches at least 7 cm in diameter (limit for coarse wool) of all trees and shrubs  starting at 12 cm dbh, based on the stem-form function according to Kaufmann (2001). The definition of the assortment is based on the 2010 edition of the Trading Practices (Handelsgebräuchen Ausgabe 2010).    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-volume_of_dead_wood-24_1.0	Volume of dead wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818001-ENVIDAT.umm_json	"Volume of stemwood with bark of all dead trees and shrubs (standing and lying)  starting at 12 cm dbh.  Unlike this theme , the ""Amount of deadwood according to the method of NFI3"" includes all lying deadwood  starting at 7 cm in diameter.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_"	proprietary
-volume_of_dead_wood_nfi1-249_1.0	Volume of dead wood NFI1	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818053-ENVIDAT.umm_json	Volume of stemwood with bark of all dead trees and shrubs (standing and lying)  starting at 12 cm dbh  recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
 voyages_2	List of voyages and station parties between 1947 and 1989 in which Australians participated, including winter and some summer personnel	AU_AADC STAC Catalog	1947-01-01	1989-12-31	62.86, -68.581, 158.977, -52.95	https://cmr.earthdata.nasa.gov/search/concepts/C1214311442-AU_AADC.umm_json	This document contains detailed descriptions of Antarctic and subantarctic voyages undertaken by Australians or in which Australians participated in between 1947 and 1989.  It also includes lists of wintering personnel at Heard Island, Macquarie Island, Mawson, Casey, Davis, Wilkes and various field parties.  Some information about summer personnel has also been recorded.  The voyages are presented in chronological order, and contain information such as the name of the ship, dates of the voyage, destination, ship's master, and personnel details.  The document also contains some details of Antarctic and subantarctic flights undertaken in support of the voyages (e.g. by the RAAF - Royal Australian Air Force).  A second file (a spreadsheet) provides the number of personnel wintering at ANARE (Australian National Antarctic Research Expeditions) stations between 1948 and 1982.  These stations include Heard Island, Macquarie Island, Davis, Wilkes, Repstat (Replacement Station at Wilkes), Casey and the Amery Ice Shelf.	proprietary
 waddington_0352584_Not provided	A Unique Opportunity for In-Situ Measurement of Seasonally-Varying Firn Densification at Summit, Greenland	SCIOPS STAC Catalog	2004-01-01	2009-01-01	-38.6, 72.5, -38.4, 72.7	https://cmr.earthdata.nasa.gov/search/concepts/C1214595086-SCIOPS.umm_json	This is a collaborative proposal by Principal Investigators at the University of Washington and the Desert Research Institute. They will make detailed measurements of the temporal and spatial variations of firn compaction to advance knowledge and understanding of ice deformation and across different fields, including remote sensing, snow morphology, and paleoclimatology. They will make detailed measurements through two winter and three summer seasons at Summit Greenland using the concept of Borehole Optical Stratigraphy, which will use a borehole camera to record details of the wall. These details can be tracked over time to determine vertical motion and strain, which in the shallow depth is dominated by firn compaction. Quantitative understanding of firn compaction is important for remote-sensing mass-balance studies, which seek to measure and interpret the changing height of the ice sheet; the surface can rise due to snow accumulation, and fall due to ice flow and increased densification rates. Quantitative knowledge of all three processes is essential. Evidence suggests that the rate of densification undergoes a seasonal cycle, related to the seasonal cycle of temperature. When interpreting ice core trapped-gas data for paleoclimate, it is important to know at what point the gas was actually trapped in the ice. The pores do not close off until deep in the firn, leading to a difference between the age of the ice and the age of the trapped gas. If summer high temperatures have more impact on compaction than mean annual temperatures, the gas-age/ice-age offset might be incorrectly calculated. Greater understanding of firn densification physics will help the interpretation of these records.   This data covers accumulation rates occurring between 1980-2008, and the data were collected between 2004-2008.	proprietary
-waldinventursihlwald_1.0	Supplementary Data Sample Plot Inventory Sihlwald	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.552084, 47.2538697, 8.552084, 47.2538697	https://cmr.earthdata.nasa.gov/search/concepts/C2789818127-ENVIDAT.umm_json	# Supplementary Data Sample Plot Inventory Sihlwald       The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label ‘Nature discovery park’ (‘Naturerlebnispark’). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003.   This dataset contains supplementary tables for the publication by Brändli et al. (2020). The metadata file describes the structure of the tables.	proprietary
-water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0	Water availability of Swiss forests during the 2015 and 2018 droughts	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817096-ENVIDAT.umm_json	The Swiss forests' water availability during the 2015 and 2018 droughts was modelled by implementing the mechanistic Soil-Vegetation-Atmosphere-Transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information and measured soil matric potential and eddy covariance data. Data include   1) csv of soil matrix potential and eddy covariance data, 2) csv of posterior model parameters, 3) geotiffs of plant-available water storage capacity until 1m soil depth and the potential rooting depth, 4) geotiffs of yearly average (2014-2019) of precipitation (P), actual evapotranspiration (ETa), evaporation as the sum of soil, snow and interception evaporation (E), actual transpiration (Ta), runoff (F) and total soil water storage (SWAT), 5) csv of simulated root water uptake aggregated for different soil depths per deciduous and coniferous trees across Switzerland at daily resolution and cumulative root fraction per soil depth for coniferous and deciduous sites, 6) geotiffs of the ratio of actual to potential transpiration (-) as mean of non-drought years 2014, 2016, 2017, 2019 and 2015 and 2018 for the month June, July, August, September and October, 7) geotiff of mean soil matric potential in the rooting zone in August 2018, 8) geotiffs of gravitational water capacity (mm) until 1 m soil depth and the maximum rooting depth (mrd), 9) geotiffs of uncertainties of the available water storage capacity (AWC) until 1m soil depth and the mean maximum rooting depth (mrd), 10) csv of average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest.	proprietary
-water-isotopes-plynlimon_1.0	Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	-3.7631607, 52.418789, -3.6402512, 52.4982845	https://cmr.earthdata.nasa.gov/search/concepts/C2789817232-ENVIDAT.umm_json	The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.	proprietary
 wbandimpacts_1	ACHIEVE W-Band Cloud Radar IMPACTS	GHRC_DAAC STAC Catalog	2023-01-23	2023-03-01	-72.861, 41.368, -71.655, 42.268	https://cmr.earthdata.nasa.gov/search/concepts/C3247862082-GHRC_DAAC.umm_json	The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format.	proprietary
-weather-snowpack-danger_ratings-data_1.0	Weather, snowpack and danger ratings data for automated avalanche danger level predictions	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817371-ENVIDAT.umm_json	Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.  These data are the basis of the following publication:  Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.	proprietary
-weather-station-wolfgangpass_1.0	Weather Station Davos Wolfgang	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.853594, 46.835577, 9.853594, 46.835577	https://cmr.earthdata.nasa.gov/search/concepts/C2789817645-ENVIDAT.umm_json	The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).	proprietary
-weather_station_klosters_1.0	Weather Station Klosters	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.880413, 46.869019, 9.880413, 46.869019	https://cmr.earthdata.nasa.gov/search/concepts/C2789817512-ENVIDAT.umm_json	A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).	proprietary
 wed_sat_99_1	Behaviour of Weddell seals during winter recorded by satellite tracking.	AU_AADC STAC Catalog	1999-06-09	1999-12-06	78, -69, 78, -69	https://cmr.earthdata.nasa.gov/search/concepts/C1214314116-AU_AADC.umm_json	This data set contains the results from a study of the behaviour of Weddell seals (Leptonychotes weddelli) at the Vestfold Hills, Prydz Bay, Antarctica. Three satellite transmitters were deployed on tagged female Weddell seals at the Vestfold Hills mid-winter (June) 1999. The transmitters were recovered in December, late in the pupping season. In total, the three transmitters were deployed and active 170 days, 175 days and 180 days. I used the first two classes of data to get fixes with a standard deviation less than 1 km. Most seal holes were more that 1 km apart (see Entry: wed_survey) so at this resolution we can distinguish between haul-out sites. We examine the number and range of locations used by the individual seals. We use all data collectively to look at diurnal and seasonal changes in haul-out bouts. None of the seals were located at sites outside the area of fast ice at the Vestfold Hills, although one seal was sighted on new fast-ice (20 - 40 cm thick). Considering the long bouts in the water, and that we only tracked haul-out locations, the results do not eliminate the possibility that the seals made long trips at sea.  The original data are stored by the Australian Antarctic Division in the ARGOS system on the mainframe Alpha. The transmitter numbers are 23453, 7074 and 7075.	proprietary
-weird_1.0	Quantifying Surface Heat Exchange over Heterogeneous Land Surfaces at Ultra-High Spatio-Temporal Resolution	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	9.9481845, 46.8594029, 9.9481845, 46.8594029	https://cmr.earthdata.nasa.gov/search/concepts/C2789817746-ENVIDAT.umm_json	The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high resolution thermal infrared camera pointing at synthetic screens, a 30Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen’s surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind speed reflect multiple layers of different static stability within 2m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind speed profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes.  Here you find the data and the documented code used to create the plots in the publication.	proprietary
-wetlands-of-zurich_1.0	Wetlands of the canton of Zürich (Switzerland): Data on species richness and recent and historic area and connectivity of 55 fens	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	8.3551025, 47.0301987, 9.2120361, 47.6407189	https://cmr.earthdata.nasa.gov/search/concepts/C2789817856-ENVIDAT.umm_json	This dataset includes data on species richness of vascular plants and bryophytes in 55 wetlands of the canton of Zürich (Switzerland) as well as recent and historic data on the area and connectivity of these 55 wetlands and was used for the paper Jamin A., Peintinger M., Gimmi U., Holderegger R., Bergamini A. (2020) Evidence for a possible extinction debt in Swiss wetland specialist plants. Ecology and Evolution.  Species richness data are available for vascular plants and bryophytes. The field survey was carried out between June 5 and August 10, 2012. The survey covered all wetland (fen) types in the canton of Zürich. For data collection, at least half a day per wetland was spent searching for species. Within each wetland all different vegetation types were covered until no new species were found to get as complete species lists as possible. In the Excel file information on species richness of the following groups is provided: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix 1a of the wetland inventory of Switzerland (BUWAL, 1990).   Based on the data of Gimmi et al. (2011), the area of all wetlands in 1850, 1900, 1950 and 2000 were determined as well as the wetland area within buffers 2km in radius with the center of the wetland as starting point. These data are also provided in the Excel sheet.  Moreover, for each wetland mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants were calculated and are provided in Excel sheet. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.	proprietary
-wfj-cal_1.0	WFJ_CAL: Calibration dataset for snow models	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C3226083165-ENVIDAT.umm_json	During the development of DAISY, the snowpack model we  realised that we did not have enough accurate calibration measurements. We  needed more reliable measurements of snow temperatures and settlements  within the snow cover.    Therefore, from winter 1990/91 the thermal development of the season snow  cover in the test field with self-developed temperature harps was measured.  These temperature harps can move freely with the snow cover, in contrast to  the usually fixed temperature profiles.    With these harps, it became possible to monitor the temperatures and  settlements of the individual layers throughout the winter.    Additionally, the surface temperature, snow level and the usual  meteorological parameters such as air temperature, humidity, wind speed and  the radiation in various wavelength ranges were measured.    Furthermore, conventional snow profiles were recorded with measurements of  densities, hardness, grain sizes and grain shapes.    During three winters, this facility was intensively used for monitoring  purposes. The support and monitoring of these measurements and the  accompanying, very time-intensive manual measurements were carried out by  Peter Weilenmann and Franz Herzog.    The results of these measurements in winter 1990/91, 1991/92 and 1992/93  are given in the internal report No. 723.    The use of these measurements for the validation of DAISY and MiniDAISY are  gathered internally in report No. 724..726.	proprietary
-wfj2_1.0	WFJ2: Snow measurements from the Weissfluhjoch research site, Davos	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	9.809568, 46.829598, 9.809568, 46.829598	https://cmr.earthdata.nasa.gov/search/concepts/C3226083117-ENVIDAT.umm_json	This dataset provides HS, TSS and TS50, TS100, TS150 at the station WFJ2 situated on the Weisfluhjoch research site (2536 m asl). It has been created from merging ENET and IMIS datsets to form a continuous timeseries from 1992- present. ENET is at 1 h resolution whereas IMIS is 30 min. This is a level 2 dataset as defined [here](http://models.slf.ch/p/dataset-processing/).	proprietary
-wfj_ice_layers_1.0	WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.8093855, 46.8297006, 9.8093855, 46.8297006	https://cmr.earthdata.nasa.gov/search/concepts/C2789817883-ENVIDAT.umm_json	The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.	proprietary
-wfj_rhossa_1.0	WFJ_RHOSSA: Multi-instrument stratigraphy data for the seasonal evolution of an alpine snowpack	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	9.8093934, 46.8296448, 9.8093934, 46.8296448	https://cmr.earthdata.nasa.gov/search/concepts/C2789817928-ENVIDAT.umm_json	The WFJ_RHOSSA dataset contains multi-instrument, multi-resolution snow stratigraphy measurements  for the seasonal evolution of the snowpack from the Weissfluhjoch research site, Davos, Switzerland.    The measurements were initiated during the RHOSSA field campaign conducted in the winter season 2015–2016 with a focus on density (RHO) and specific surface area (SSA) measurements.    The Instruments and methods used in the campaign at different spatial and temporal resolution are: SnowMicroPen, Density Cutter, IceCube, Traditional profiles, Stability tests and X-ray tomography. The measurements are complemented by simulation data from the model SNOWPACK.	proprietary
 white_model_parameters_652_1	Literature-Derived Parameters for the BIOME-BGC Terrestrial Ecosystem Model	ORNL_CLOUD STAC Catalog	1947-06-15	2000-06-21	-180, -90, 180, 90	https://cmr.earthdata.nasa.gov/search/concepts/C2810678753-ORNL_CLOUD.umm_json	Various aspects of primary production of a variety of plant species found in natural temperate biomes were compiled from literature and presented for use with process-based ecosystem simulation models or ecosystem studies.  Information was selected according to the input parameter needs of the BIOME-BGC process-based simulation model.	proprietary
 whitney_dem_1	A digital elevation model (DEM) and orthophoto of the Whitney Point area of the Windmill Islands, Antarctica	AU_AADC STAC Catalog	2005-01-01	2007-05-01	110.522, -66.255, 110.544, -66.248	https://cmr.earthdata.nasa.gov/search/concepts/C1214311446-AU_AADC.umm_json	This dataset includes a 10 metre resolution digital elevation model (DEM) of the Whitney Point area of the Windmill Islands, Antarctica and an orthophoto created using the DEM.  The data are stored in the UTM zone 49 map projection.  The horizontal datum is WGS84.   The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins.  See a related URL below for a map showing Whitney Point.	proprietary
 wilhend_687_1	LBA Regional Vegetation and Soils, 1-Degree (Wilson and Henderson-Sellers)	ORNL_CLOUD STAC Catalog	1900-01-01	1999-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2777328977-ORNL_CLOUD.umm_json	This data set is a subset of a global vegetation and soils data set by Wilson and Henderson-Sellers (1985a). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format.The original global data set (Wilson and Henderson-Sellers 1985a) is an archive of soil type and land cover data derived for use in general circulation models (GCMs). The data were collated from maps depicting natural vegetation, forestry, agriculture, land use, and soil, and they were archived at a resolution of 1 latitude by 1 longitude. The data set indicates soil type, soil data reliability, primary vegetation, secondary vegetation, and land cover data reliability. Approximately 50 land cover classifications are used, including categories for agricultural and urban uses. The inclusion of secondary vegetation type is particularly useful in areas with cover types that may have a fragmented distribution, such as in areas of urban development. The soil type data are classified according to climatically important properties for GCMs, and they indicate color (light, medium, or dark), texture, and drainage quality of the soil. The land cover data are compatible with the soils data, forming a coherent and consistent data set. The reliability of the land cover data is ranked on a scale of 1 to 5 (high to low). The reliability of the soil data is ranked as high, good, moderate, fair, or poor.Recommendations for the use of these data, as well as more detailed information can be found in Wilson and Henderson-Sellers (1985b).Further data set information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/wilhend/comp/wilhend_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.	proprietary
 willmott_673_1	LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott and Webber)	ORNL_CLOUD STAC Catalog	1960-01-01	1990-12-31	-85, -25, -30, 10	https://cmr.earthdata.nasa.gov/search/concepts/C2779732234-ORNL_CLOUD.umm_json	"This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10 N to 25 S, 30 to 85 W. The data are in ASCII GRID format. The data consist of the following: Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at ""Willmott, Matsuura, and Collaborators' Global Climate Resource Pages"" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for ""Available Climate Data,"" register or sign in, and follow the link for ""South American Climate Data."" Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf. "	proprietary
-wind-topo_model_0.1.0	Wind-Topo_model	ENVIDAT STAC Catalog	2022-01-01	2022-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817956-ENVIDAT.umm_json	"Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain.    It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input.    This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM.    Accompanies the publication ""Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning"" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265  Please cite this publication if you use Wind-Topo or derive new models from it.  The code can be used under the GNU Affero General Public License (AGPL)."	proprietary
 wind_dem_1	Digital Elevation Model of the Windmill Islands	AU_AADC STAC Catalog	1999-07-11	1999-08-23	110, -67, 111, -66	https://cmr.earthdata.nasa.gov/search/concepts/C1214311463-AU_AADC.umm_json	This DEM includes all the inshore and offshore islands, all the peninsulas and the lower slopes of the icecap leading up to Law Dome. The DEM has a cell size of 10 m.	proprietary
 windmill_bathy_surveys_1	Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands	AU_AADC STAC Catalog	1997-02-01	1997-03-31	110.515, -66.297, 110.565, -66.258	https://cmr.earthdata.nasa.gov/search/concepts/C1214311438-AU_AADC.umm_json	Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands.  This dataset resulted from bathymetric surveys of  Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands, carried out in February and March 1997 as part of ASAC Project 2201. The surveys were carried out by Jonny Stark and Tim Ryan in the workboat the 'Southern Comfort'.	proprietary
 winston_bathy_1	A bathymetric survey of Winston Lagoon	AU_AADC STAC Catalog	1987-01-09	1987-01-14	73.23557, -53.20274, 73.83911, -52.95006	https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json	During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted.  Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather.  The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m.	proprietary
 wisperimpacts_1	Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS	GHRC_DAAC STAC Catalog	2020-01-18	2023-02-28	-95.2426928, 33.2614038, -67.8781539, 48.2369386	https://cmr.earthdata.nasa.gov/search/concepts/C2175816611-GHRC_DAAC.umm_json	The Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS dataset consists of condensed water contents, water vapor measurements, and isotope ratios in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in ASCII format from January 18, 2020, through February 28, 2023.	proprietary
-wml_bilderstudie_1.0	Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818010-ENVIDAT.umm_json	"This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the ""WaMos meets LFI"" (WML) project."	proprietary
-wmlganzeschweiz_1.0	WaMos meets LFI, ganze Schweiz	ENVIDAT STAC Catalog	2021-01-01	2021-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818071-ENVIDAT.umm_json	The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted.	proprietary
-wood-mobilization-survey_1.0	Wood Mobilization Survey	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789818147-ENVIDAT.umm_json	Understanding the market behavior of forest owners and managers is important to identify effective and efficient policy instruments that enhance wood provisioning. We conducted a choice experiment (CE) at two study sites in south-eastern Germany (Upper Bavaria and Lower Franconia) and two in north-eastern Switzerland (Grisons and Aargau) to elicit foresters’ preferences for different supply channels, contract lengths, wood prices and duration of business relations.   CE belong to the stated preference methods to analyze individual decision making. Respondents had to choose among three options based on different attribute levels in 12 consecutive choice sets.   Our study site comparison identified regional differences and particularities, which should be taken into account when promoting wood mobilization. The success of policy instruments, such as the promotion of bundling organizations and long-term contracts, can vary depending on the specific structural and institutional conditions, like existing marketing channels, as well as on behavioral aspects of the particular public and private decision makers.	proprietary
 woody_biomass_657_1	Woody Biomass for Eastern U.S. Forests, 1983-1996	ORNL_CLOUD STAC Catalog	1983-01-01	1996-12-31	-100, 25, -60, 50	https://cmr.earthdata.nasa.gov/search/concepts/C2808093948-ORNL_CLOUD.umm_json	Estimates of the woody biomass density and pools were derived at the county scale of resolution of all forests of the eastern United States using new approaches for converting inventoried wood volume to estimates of above and belowground biomass.	proprietary
 wrfimpacts_1	Weather Research and Forecasting (WRF) Model IMPACTS	GHRC_DAAC STAC Catalog	2020-01-12	2023-03-04	-114.2019958, 22.9705658, -53.7980042, 53.5889359	https://cmr.earthdata.nasa.gov/search/concepts/C1995874860-GHRC_DAAC.umm_json	The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.	proprietary
-wsl-drought-initiative-2018_1.0	Litterfall and pollen data of three LWF beech plots	ENVIDAT STAC Catalog	2019-01-01	2019-01-01	6.65804, 46.58377, 9.06707, 47.22516	https://cmr.earthdata.nasa.gov/search/concepts/C2789818298-ENVIDAT.umm_json	This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018).    Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen).    __Datafile:__     _LWF_beech_plots_litterfall_pollen.xlsx_    1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion.    2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models.	proprietary
-wslintern-article-envidat-supports-open-science_1.0	EnviDat Supports Open Science	ENVIDAT STAC Catalog	2020-01-01	2020-01-01	8.4546488, 47.3605728, 8.4546488, 47.3605728	https://cmr.earthdata.nasa.gov/search/concepts/C2789818383-ENVIDAT.umm_json	"The article ""EnviDat Supports Open Science"" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support.     License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 ""No Rights Reserved"" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions."	proprietary
 wygisc_wolphoyo_Not provided	Aerial Photos for Crazy Woman and Clear Creek Watersheds	SCIOPS STAC Catalog	1970-01-01		-107, 44, -106.36, 44.75	https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json	The purpose of this data was to provide a base layer of aerial photos       at the watershed scale for two areas used as part of a the Wyoming       Open Land pilot area.              Digital and registered aerial photos of Crazy Woman and Clear Creek       Watersheds, Wyoming. Each photo represents approximatley one-quarter       of a U.S.G.S. Topographic map (north-east, north-west, south-each and       south-west quarters). TIFF image format.	proprietary
-yield-15_1.0	Yield	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817175-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_and_mortality-13_1.0	Yield and mortality	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817288-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were felled, died or disappeared between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_and_mortality_star-163_1.0	Yield and mortality*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817402-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh that were used, died or disappeared between two inventories.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.     __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_of_live_bole_wood-87_1.0	Yield of live bole wood	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817550-ENVIDAT.umm_json	Volume of stemwood at least 7 cm in diameter (limit for coarse wood) without the bark and stump that were  living trees or shrubs  starting at 12 cm dbh in the pre-inventory and were cut between two inventories.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_of_merchantable_branches-112_1.0	Yield of merchantable branches	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817674-ENVIDAT.umm_json	Wood volume of branches with bark at least 7 cm in diameter (limit for coarse wood) of all living trees and shrubs  starting at 12 cm dbh that were present in the pre-inventory and cut meanwhile.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_of_merchantable_timber-114_1.0	Yield of merchantable timber	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817810-ENVIDAT.umm_json	Wood volume of the stem (without bark and stump) and the branches (with bark) at least 7 cm in diameter (limit for coarse wood) from trees and shrubs  starting at 12 cm dbh that were living in the pre-inventory and were cut between the two inventories.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-yield_star-161_1.0	Yield*	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817861-ENVIDAT.umm_json	Volume of stemwood with bark of all trees and shrubs  starting at 12 cm dbh cut between two inventories.  *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary
-young_forest_with_browsing_damage-193_1.0	Young forest with browsing damage	ENVIDAT STAC Catalog	2018-01-01	2018-01-01	5.95587, 45.81802, 10.49203, 47.80838	https://cmr.earthdata.nasa.gov/search/concepts/C2789817885-ENVIDAT.umm_json	Number of regeneration trees where browsing of the shoots from the previous year was recorded in NFI’s regeneration survey.    __Citation:__   > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_	proprietary